Ketone measurement analysis using baseline levels

ABSTRACT

A portable system is provided for measuring a ketone, such as an acetone, in the breath or other bodily fluid of a user. The system includes a portable measurement device that analyzes fluid samples and generates corresponding ketone measurements. The portable measurement device communicates with an application which runs on a smartphone or other mobile device of the user. The application tracks, and generates graphs of, the ketone measurements, and may include various features for facilitating the analysis of the measurements. One such feature compares ketone measurements taken while the user is on a health program to a baseline level determined from pre-program-initiation ketone measurements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/362,686, filed Nov. 28, 2016, which is a continuation of U.S. patentapplication Ser. No. 15/131,985, filed Apr. 18, 2016 (now U.S. Pat. No.9,504,422), which is a continuation of U.S. application Ser. No.14/807,821, filed Jul. 23, 2015 (now U.S. Pat. No. 9,351,684), whichclaims priority to U.S. Provisional Patent Appl. Nos. 62/027,851, filedon Jul. 23, 2014, 62/150,376, filed on Apr. 21, 2015, and 62/161,782,filed on May 14, 2015. The disclosures of the aforementionedapplications are hereby incorporated in their entirety herein byreference.

TECHNICAL FIELD

The present disclosure relates generally to devices, systems and methodsfor measuring one or more ketones in one or more body fluids (e.g.,blood, urine, breath, or some combination of these), preferably acetonein human breath. It may be used in fields of weight loss, weightmanagement, general health and wellness and in management ofketoacidosis.

BACKGROUND

Obesity is a major individual and public health concern in the UnitedStates and throughout the world. In the United States alone,approximately 33% of the adult population is obese and another 33% areoverweight. Treatment of obesity and other weight-related disordersinvolves a multi-factorial approach (typically a combination of diet,exercise, behavioral health modifications and sometimes medication orsurgery) and commonly requires significant and sometimes permanentlifestyle modification. Especially in adults, the oft-required lifestylechanges can make obesity an extremely difficult condition to overcome.

The main goal of obesity management is reducing the amount of fat in thebody. For various reasons (to motivate subjects, to enforce complianceand to troubleshoot/customize diets), it is useful and important to havea means to track and trend fat metabolism.

The need for lifestyle changes is not limited to treatment of obesity oroverweight. As an example, individuals suffering from other metabolicconditions, such as elevated cholesterol or high blood pressure, maybenefit from improving their diet or changing exercise patterns. Agrowing number of individuals seek to reduce their carbohydrate intaketo increase utilization of fat as an energy source, in hopes of reducingtheir overall insulin usage and thereby counteracting metabolicabnormalities (such as high blood pressure).

Athletes and fitness-conscious individuals are concerned about stayingin peak physical condition, and are often actively engaged in structuredsports activities (whether professional or not). Such individualsstruggle with making data-driven decisions about how best to optimizetheir biochemical and physical condition. They often try to make “smart”decisions about how best to reach their fitness or health goals.

Anorexia nervosa is a psychiatric disorder having substantialimplications and is oftentimes a lifelong illness. The disorder is mostprevalent in adolescents and young adults, and is 90% more common inyoung women than men. Because of the complex nature of the disorder andthe significant level of mental health treatment, treatment of anorexianervosa is most effective in-center and is correspondingly expensive.Improving patient outcomes requires considerable counseling andmonitoring.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate a presently preferred embodimentsand methods of the present disclosure and, together with the generaldescription given above and the detailed description of the preferredembodiments and methods given below, serve to explain the principles ofthe present disclosure. Of the drawings:

FIG. 1 is a block diagram of a system according to a presently preferredembodiment of the invention according to an aspect.

FIG. 2 is a pictorial diagram of another system according to a presentlypreferred embodiment of the present disclosure according to an aspect.

FIG. 3 is a pictorial diagram of another system according to a presentlypreferred embodiment of the present disclosure according to an aspect.

FIG. 4 is an elevated perspective view of a base and breath bag for thesystem of FIG. 3.

FIG. 5 is a cross sectional cutaway side view of the base shown in FIG.4.

FIG. 6 is a network diagram for the systems of FIGS. 1-3.

FIG. 7 is a pictorial view of display panels of a software applicationaccording to a presently preferred method implementation in accordancewith another aspect of the present disclosure.

FIG. 8 shows a scatter plot display output of breath acetone measurementdata provided by the software application, display panels for which areshown in FIG. 7.

FIG. 9 provides illustrative inputs for a Settings panel of the softwareapplication, display panels for which are shown in FIG. 7.

FIG. 10 shows a graphical chart displaying breath acetone levels andvarious events.

FIG. 11 is a flow chart or diagram that illustrates process flows anddisplays for the software application, display panels of which are shownin FIG. 7.

FIG. 12 is a pictorial view of a timing diagram and associated softwaredisplay panels for providing interactive reminders according to apresently preferred implementation of the present disclosure using thesoftware application, display panels for which are shown in FIG. 7.

FIG. 13 is a display panel for an electronic device used to selectacetone factors according to a presently preferred embodiment of thepresent disclosure according to another aspect.

FIG. 14 is a display panel for presenting selected acetone tags in thepreferred method implementation associated with the display of FIG. 14.

FIG. 15 a scatter plot display output of breath acetone measurement dataprovided by the software application, display panels for which are shownin FIG. 7, and displaying the data according to acetone tags of FIG. 14.

FIG. 16 shows a graphical chart displaying breath acetone levels andvarious events.

FIG. 17 is a pictorial diagram that illustrates the inputting of triggerpoints into a system according to a presently preferred embodiment ofanother aspect of the present disclosure.

FIGS. 18A-E show graphical charts utilizing various algorithms torepresent breath acetone levels and various event tags according to theimplementation that is the subject of FIG. 17.

FIG. 19 shows a graphical chart that uses a given algorithm tore-present raw breath acetone levels for enhanced reporting to ahealthcare provider according to preferred implementations of thepresent disclosure.

FIG. 20 depicts the data of FIG. 19 with certain features highlighted.

FIG. 21 shows a graphical chart that uses a given algorithm tore-present raw breath acetone levels for enhanced reporting to ahealthcare provider.

FIG. 22 depicts the data of FIG. 21 with certain features highlighted.

FIG. 23 illustrates a breath acetone measurement device base unit.

FIG. 24 illustrates a breath acetone measurement device that usesnanoparticle-based sensors.

FIG. 25 illustrates a breath acetone measurement device that usesnanoparticle-based sensors and a breath bag instead of a mouthpiece.

FIG. 26 is an embodiment of an integrated mouthpiece for use inconjunction with an electrochemical breath acetone measurement device.

FIG. 27 illustrates a detachable component detection state diagram,according to one embodiment.

FIGS. 28A-28C illustrate a user interface displayed by the electronicdevice that depicts a status of the insertion of detachable components.

FIGS. 29A-29B illustrate another example in which the detachablecomponents are inserted in a different (and incorrect) order.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Reference will now be made in detail to the presently preferredembodiments and methods of the present disclosure as described hereinbelow and as illustrated in the accompanying drawings, in which likereference characters designate like or corresponding parts throughoutthe drawings. It should be noted, however, that the present disclosurein its broader aspects is not limited to the specific details,representative devices and methods, and illustrative examples shown anddescribed in this section in connection with the preferred embodimentsand methods. The present disclosure according to its various aspects isparticularly pointed out and distinctly claimed in the attached claimsread in view of this specification, and appropriate equivalents.

Ketones are useful to track and trend progress on a fat loss program.Ketone concentrations in the body and in body fluids (e.g., blood,urine, breath, or some combination of these) are correlated with fatmetabolism. In the body, three main “ketone bodies” are generated:acetoacetic acid, β-hydroxybutyric acid, and acetone. In the case ofacetone, because it is volatile, it is released into alveolar air whenthe blood brings it in contact with the lungs.

Generally, ketone levels increase in relation to increases in the body'sfat metabolism. Higher ketone concentrations indicate greater metabolismof fat. During a net caloric deficit, greater fat metabolism portendsincreased fat loss (e.g., a primary objective in weight reduction).Measurement of ketones offers the potential to be a powerful andaccurate measure of fat metabolism, and therefore provides manyadvantages in the management of metabolic conditions and obesitymanagement.

Another instance in which measurement of breath acetone is useful is forthe management and treatment of Type 1 diabetics. In Type 1 diabetics,where there is insufficient intracellular sugar or where sugarmetabolism is impaired, the body resorts to metabolizing fat. Thisincrease in fat metabolism can result in substantial and, in this case,unwanted increases in ketone production. Because ketones are acidic,their build-up in the blood stream can cause a downward shift in bloodpH, assuming that the body's ability to buffer pH is compromised oroverwhelmed. This can result in a condition called diabeticketoacidosis. In addressing this phenomenon, the objective is to monitorketone levels to detect its onset and to aid in its management.

In using breath acetone analysis as a management tool for these andother like applications, it is important that the ketone measurement bereliable. Ideally, ketone concentrations in the blood and in the breathare highly correlated with fat metabolism and correspondingly ketonegeneration in the liver, so that breath acetone measured in exhaledbreath is an accurate proxy.

The present inventor(s) has discovered that a certain level ofvariability not infrequently arises in making breath ketone measurementsand in related analyses. A number of variables can affect thecorrelation between the breath-measured ketone levels and fatmetabolism.

Another concern is patient or user compliance with the weight managementprogram, and the related concern that ketone measurements may be areflection of patient non-compliance with the program rather than withactual metabolic phenomena.

Traditional compliance with a weight management program has beenmonitored through the patient's or user's maintenance of a journal. Eachday over the course of the program, the user records in the journal thedetails of food and beverage consumption, including, for example, whatwas eaten, when, how much, and/or the like.

This journaling approach, however, is unreliable and otherwiseproblematic, for example, in that it imposes a requirement on the userfor his or her timely and accurate recordation. In some cases there alsomay be a risk of intentional concealment of non-compliance (colloquiallycalled “cheating”).

Accordingly, there is a need for ketone measurement devices, systems andmethods that accurately measure and report metabolic processes relatingto fat, that decrease the vulnerability to user non-compliance and otheraberrant factors, and that decrease the likelihood of intentional orwilling user non-compliance.

The present disclosure comprises methods, systems, and devices that useketone, such as ketones found in bodily fluids (e.g., blood, urine,breath, combinations of these, etc.) or detected via permeation throughthe skin, in conjunction with novel software, algorithms, and processesto improve measurement results and correspondingly weight managementprogram results. These methods, systems, and devices can facilitate usercompliance, provide more lucid and relevant measurement information andfeedback to the user and supportive third parties, and limit or preventunwanted masking of data. Improved health outcomes can be achieved byincorporating a ketone measurement system that affords a robust novelmethod for tracking user behavior, correlating behavior with the ketonelevels to generate user-specific feedback, and reporting this feedbackto the user and/or his or her healthcare provider in a novel,highly-useful and less ambiguous fashion.

For simplicity, the ketone measurement system is described herein as abreath acetone measurement system that includes a breath acetonemeasurement device that measures acetone in breath. The breath acetonemeasurement system may further include a mobile communications devicethat executes an application and communicates with the breath acetonemeasurement device to provide the functionality described below.However, this is not meant to be limiting. The ketone measurement systemdescribed herein can include a ketone measurement device that measuresany number of different types of ketones (including, for example,acetone) in any number of different types of body fluids (including, forexample, blood, urine, breath, and/or any combination of these) or viapermeation through the skin. The ketone measurement device may interactwith the mobile communications device in the same or similar manner asdescribed herein with respect to the breath acetone measurement device.

The methods, systems, and devices described herein can be used tomotivate favorable behavioral changes related to weight loss in a useron a treatment program. Various treatment programs may be used withthese methods. Examples include calorie-restricted diets, diets withfixed macronutrient composition (e.g., low carbohydrate diets or highprotein diets), cardiovascular exercise, weight bearing or resistanceexercise, administering pro-lipolytic medications, administering weightloss drugs, and combinations thereof.

When attempting to control a user's diet or weight (e.g., for weightmanagement, diabetic ketoacidosis (“DKA”) monitoring or prevention,etc.), it may be common for certain rules to be set regarding the user'sactivities. The rules may be set by a third party using a browser (e.g.,a web browser) executing on an electronic device, a network-accessibleapplication (e.g., an application executing on a server), and/or thelike. For example, the third party may use the browser to log into anetwork or web-based application or server (e.g., via a content page)that allows the third party to view, create, select, and/or set rules.Some of these rules may relate to the user's diet. Others may relate tothe breath acetone measurement regime. In each instance and for eachsubject matter, there may be favored, disfavored, and prohibited items.Examples of dietary rules are provided in Table 1, and examples ofacetone measurement-related rules are provided in Table 2.

TABLE 1 Examples of Dietary Rules Subject Matter Permitted DisfavoredProhibited Foods Carbohydrates More than 50 grams per day Protein Morethan 100 grams per day Beverages Water Unlimited Soft Drinks No softdrinks Alcoholic No alcoholic Beverages beverages Energy Drinks One 12oz. energy drink per week Meal or Eating Times Eating after No eatingafter 7 pm 7 pm

TABLE 2 Examples of Acetone Measurement-Related Rules Subject MatterPermitted Disfavored Prohibited Time of Day Test each morning beforefood or beverages other than water; and each afternoon between 4 pm and5 pm, no food of beverages other than water for two hours before thetest. Medication Perform measurement reading at least two hours beforemedication. Exercise Perform measurement reading at least two hoursbefore exercise.

The effectiveness of a program can be significantly impacted by theextent to which the user complies with the program rules. Infrequent orpoorly controlled compliance with acetone measurement rules or protocolscan create interpretation challenges regarding measurement results,which in turn detracts from straightforward and simple use by the userhimself or herself. Conversely, where the user faithfully complies withthese rules, valuable data and feedback can be provided throughout theprogram and program success can be greatly improved.

The benefits of ketone measurement in addressing weight management, andin managing such concerns as diabetic acidosis, are highlighted hereinabove. The benefits can be even more pronounced when the measurement isof breath acetone, as opposed to measurement of blood or urine ketoneconcentration. Breath measurement avoids the often unpleasant need todraw a blood sample or collect a urine sample, and of handling thesamples. The relative ease, speed, and convenience of breath acetonemeasurement also facilitates user compliance. Ketone measurement ingeneral, and breath acetone measurement in particular, can provideimportant progress updates and encouragement to a user during the courseof a program, which in turn can help to motivate the user not only toadhere to or comply with the program, but also to the ketone monitoringregime.

Even with breath acetone measurement, however, motivating behavioralchanges in a user, especially regarding adherence to a program, oftenposes a significant challenge. With users who are severely overweight,for example, certain adverse behaviors that are causally related totheir weight challenges may have become habitual and difficult tochange. This is not to say that all overweight users can control weightvia behavior modification, but rather that, for a large subset of users,certain events and/or foods trigger behavior that lends itself to weightgain. Examples of such behavior include eating high caloric food, eatingunhealthy food, overeating, night eating, etc.

The challenges, but also the potential benefits, of motivating andmeasuring user compliance with a program and with the associated breathacetone measurement regime are particularly pronounced given the move inthe field to private or home administration, as opposed to treatment ina clinical setting. Much of the information available about breathacetone measurements and their interpretation has been obtained inclinical settings, in which the circumstances were controlled. Thepresent applicant believes it is one of the first, if not the firstentity, to test ketone measurement, or at least to conduct systematictesting (other than an isolated test or set of tests), in a non-clinicalsetting, most notably in the user's home or environment, whereketone-impacting variables were not tightly controlled.

As a result of this testing, together with her research and involvementin the field, the present inventor(s) has learned the following:

(1) The timeliness of ketone measurements and the faithfulness of usercompliance with certain program rules regarding breath acetonemeasurement are particularly important to the success of many programs.Specifically pre-calculated reminders and interactions with the user cangreatly increase program success.

(2) Certain recurring events or conditions, referred to herein as“acetone tags,” arise during the course of a typical program that canconfound tracking of the progress on the weight loss program (or otherobjective such as fitness progress) that is sought to be monitored. Ifthese recurring factors are properly identified and accommodated, theirundesirable masking effects can be mitigated or eliminated.

(3) User-specific “trigger points” create unduly high risks of programnon-compliance. If these trigger points are identified and managed, theprobability and extent of program success is greatly increased.

(4) Computing and making accessible the user's “baseline” acetone levelshelps the user create individual-specific goals, ranges, and trends.

Thus, in summary, in the process of monitoring breath acetone levels toassess the effectiveness of a weight management or DKA monitoring orprevention program, the success of this monitoring effort can be greatlyimproved if one develops a system, method, or device that employs one ormore, and preferably all, of the following technologies:

(1) Use selective and interactive reminders and prompts to increase thelikelihood of faithful user compliance with the breath acetonemeasurement regime;

(2) Use acetone tags to limit or prevent certain recurring events frommasking true, compliant underlying ketone measurements;

(3) Use one or more trigger points to identify and accommodatenon-compliant user activity, and to discourage it; and/or

(4) Display or otherwise make accessible user baseline information topromote user-specific progress and to aid in counseling.

Presently preferred embodiments and method implementations according tovarious aspects of the present disclosure provide these features, insome instances individually and in others collectively, using systemsthat comprise a breath acetone measurement device and an electronic orcommunications device in operative communication with the measurementdevice, operating under the control of or otherwise in conjunction witha software application or “App,” that bring these improvements andassociated benefits to bear.

System Embodiments

One such system, such as a system 10 as shown in FIG. 1, is provided formeasuring a ketone in the breath of a user. System 10, which representsa presently preferred embodiment of the present disclosure, morespecifically is designed to conduct multiple measurements over time, andto report those results as more fully described herein below.

System 10 comprises a portable integrated measurement unit 11 that inuse would be in the possession of or co-located with a user at a firstlocation.

Measurement unit 11 and the associated electronic or communicationsdevice (described herein below) are designed to be used and operated bya “user” (e.g., a dieting person, athlete, patient, etc.) whose ketoneconcentrations are being measured. It is amenable to home or office useby the user alone, for example, without the presence or assistance of afriend, aid, nurse or clinical staff, etc. Measurement unit 11 and thecommunications device also are amenable, however, to use by a personother than the user whose ketones are being measured, for example, suchas a coach, trainer, doctor, nurse, clinical technician, family member,friend and/or the like, and rather than the user's home or workplace,measurement unit 11 and the communications device may be located atother locations (e.g., a gym, a diet treatment center, a treatingphysician's office, a clinic, a laboratory, hospital, and/or the like).Thus, although the user is the person whose ketone levels are beingmeasured, the “user” may or may not also be the person who performs themanual commands and operations using unit 11 and/or the electronic orcommunications device. For simplicity and ease of illustration,throughout the detailed description section in this document, the useris assumed to be both the person whose ketones are measured and theoperator of the measurement unit and communications device are the same,even though this may not be and need not be the case in a given instanceor application of the system, unless indicated otherwise.

The “first location” preferably comprises a location at which a user ofthe system, such as the user whose ketone levels are being measured, islocated. Given the fact that the user-based unit 11 is portable, ittypically would be located with the user or users, or at a location thatis readily available to the user or users, such as the user's or users'home or workplace. Unit 11 could, however, be kept in the user's orusers' vehicle, purse, backpack, and/or the like. This first locationalso could comprise a treatment facility, such as a treating physician'soffice, hospital, outpatient clinic, and/or the like.

Unit 11 comprises a breath ketone measurement device 12 that measuresthe concentration of the ketone or ketones of interest within the user'sbreath sample. Ketones of interest in breath typically are limited toacetone, which usually is the only ketone that has a sufficiently lowmolecular weight to exist in the gas phase or is sufficiently volatileat both standard temperature and pressure and at those found in thepulmonary tissues, alveolar spaces, upper airways, and exhaled breath.In other embodiments, this unit 11 may be or comprise a blood or urineketone measurement device, such as those disclosed in U.S. patentapplication Ser. No. 14/690,756, titled “KETONE MEASUREMENT SYSTEM ANDRELATED METHOD WITH ACCURACY AND REPORTING ENHANCEMENT FEATURES” andfiled on Apr. 20, 2015, which is hereby incorporated by reference hereinin its entirety.

Sensor system 18 is designed to analyze the fluid and measure theconcentration of the ketone or ketones of interest in the fluid, and togenerate a measurement signal that is representative or indicative ofthe measurement result (e.g., here the ketone concentration in thefluid). Appropriate sensor systems include, without limitation,colorimetric sensors, enzymatic and electrochemical sensors, thermal andthermoelectric sensors (e.g., thermopile sensors), nanoparticle or metaloxide-based sensors, and so on. Examples of colorimetric sensors areprovided in U.S. Provisional Application No. 61/800,081, commonlyassigned to the assignee hereof and are hereby incorporated herein byreference as if fully set forth herein. Examples of metal oxide ornanoparticle sensors are provided in U.S. Utility application Ser. No.13/052,963, commonly assigned to the assignee hereof and are herebyincorporated by reference as if fully set forth herein. Other examplesof sensors are provided in U.S. Pat. Nos. 6,609,068 and 7,364,551,commonly assigned to the assignee hereof and hereby incorporated byreference as if fully set forth herein.

Measurement device 12 receives a sample of the user's breath andmeasures acetone in the breath sample to ascertain the presence andpreferably the concentration or concentration range of the acetone inthat sample. The term “measure” or, equivalently, “sense,” as usedherein, may comprise analysis to ascertain or detect a specificconcentration of the acetone in the breath sample, or to detect thepresence of the acetone above a specified threshold concentration, or todetect whether the concentration of the acetone is within a particularrange or ranges, and/or the like.

Measurement device 12 is capable of measuring the concentration ofacetone in a breath sample of the system user at concentration ranges,and thus with sensitivity, at which the acetone is present in the breathsample or samples as normally encountered in breath under circumstancesfor which the measurement is intended to address (e.g., weightmanagement, acidosis monitoring and management, and so on). Formeasurements in healthy users, for example, the sensitivity range of themeasurement device for acetone is within the range of normal acetonelevels for healthy users and, for measurements of users with aparticular condition (e.g., obesity, with diabetes, metabolic syndrome,etc.), the device sensitivity is within ranges expected to beencountered under those circumstances. Exemplary ranges for weightmanagement programs include 0 parts per million (“ppm”) to 10 ppm, 0 ppmto 20 ppm or 0 ppm to 60 ppm, depending on the type of the program. Ingeneral, the more the weight management encourages fat metabolism as theprimary source of fuel, the higher the range. For DKA prevention ormonitoring programs, an exemplary range is 0 ppm to 200 ppm.

Measurement device 12 optionally may comprise an input device 14 a forinputting data or information into measurement device 12. Input device14 a may comprise a keyboard, mouse, tracking device or anothertouch-sensitive device, or other device capable of inputting data andinformation as further described herein. Measurement device 12 alsooptionally may comprise an output device 14 b (e.g., a display) forpresenting user information and outputting measurement results.Preferably, input device 14 a and output device 14 b of unit 11 areintegrated in the form of a touch screen display 14. Measurement devicemay also communicate wirelessly (e.g., via Bluetooth, IEEE 802.11x,etc.) or via a wired connection with the user's portable electronicdevice (e.g., a wired telecommunications device, a wirelesstelecommunications device, or a mobile communications device, such as asmart phone, tablet, and/or the like).

Measurement device 12 also comprises a breath sample input 16 and asensing system 18. Breath sample input 16 is designed to receive asample of the breath to be analyzed. It typically will differ in designdepending on the expected concentration range for the acetone and otherfactors, for example, as described in U.S. Provisional Application No.61/800,081.

Sensing system 18 is designed to analyze the fluid and measure theconcentration of the ketone or ketones of interest in the fluid, and togenerate a measurement signal that is representative or indicative ofthe measurement result (e.g., here the ketone concentration in thefluid). Appropriate sensing systems include, without limitation,colorimetric sensors, enzymatic and electrochemical sensors, thermal andthermoelectric sensors such as thermopile sensors, nanoparticle or metaloxide-based sensors, and so on. Examples of colorimetric sensors areprovided in U.S. Provisional Application No. 61/800,081. Examples ofmetal oxide or nanoparticle sensors are provided in U.S. Utilityapplication Ser. No. 13/052,963. Other examples of sensing systems areprovided in U.S. Pat. Nos. 6,609,068 and 7,364,551.

Unit 11 also comprises an electronic device or, equivalently, acommunications device 30. Electronic device 30 is in operativecommunication with measurement device 12 and is adapted or configured toreceive the measurement signal from measurement device 12, or from anassociated processor or processing means (described more fully hereinbelow).

Measurement device 12 and electronic device 30 are in operativecommunication in the sense that they are configured to communicateinformation in the form of digital data at least from one device to theother and preferably bi-directionally between the two devices, directlyor indirectly (e.g., via an intermediate processor). This operativecommunication may be enabled through a direct connector (such as printedcircuit board connector or slot), a docking station, a cable, orwirelessly, as further described herein below. In unit 11, thecommunication between these devices is via a direct connection (e.g., bybeing affixed on a common circuit board or adjacent circuit boards withohmic connection, via a cable, and/or the like).

Electronic device 30 also comprises a capability to transmit themeasurement signal, in its raw form or in modified form as furtherdescribed herein, outwardly from unit 11. Preferably, this outward orexternal communications capability comprises data receiving capabilitiesso that electronic device 30 can transmit and receive databi-directionally, both to and from unit 11, via a wired or wirelessconnection. Presently preferred embodiments of this outward or externalcommunications feature of electronic device 30 comprise a data modem, awireless transceiver, or combinations of these. Presently preferredexamples of suitable wireless technology include the transmissioncircuitry and associated software of commercially-available cell phonesor “smartphones,” Bluetooth transceiver technology, and/or the like.Alternatively or in addition, however, the communication may comprise awired connection to a network like the Internet (e.g., via a cablemodem) or to another device like the measurement device 12 (e.g., via aUSB connection, an Ethernet connection, etc.). Electronic device 30optionally also may comprise an input device 34 a such as a keyboard,keypad, mouse, track ball, touchpad, touch screen, and/or the like, forinputting information or data, and a display 34 b (e.g., for displayinguser information, data, and/or measurement results). Presently preferredinput and display devices may comprise or be combined or associated witha touch screen 34.

In system 10, unit 11 comprises both measurement device 12 andcommunications device 30, and thus, it is generally preferred that asingle input device 36 a and a single display 36 b are used for bothdevice 12 and device 30. This obviates the need to provide separateinput and display devices for each of them, thereby potentially loweringcomplexity and unit cost. More preferably, and as in unit 11, inputdevice 36 a and display 36 b are combined in the form of a touch screen36 that serves both devices 12 and 30.

Unit 11 also optionally may comprise a clock, calendar, and a locatingdevice such as a Global Positioning Satellite (GPS) receiver.

While unit 11 is depicted as including both the measurement device 12and the electronic device 30, this is not meant to be limiting. Forexample, the measurement device 12 and the electronic device 30 may beseparate devices and the measurement device 12 and the electronic device30 may maintain communication with each other via a wired or wirelessconnection. FIGS. 2-3 depict examples in which the measurement device 12and the electronic device 30 are separate devices.

System 10 further comprises a remote system 40 disposed at or within aremote facility 42 (also referred to herein as a “center facility 42”)at a second location remote from the first location. The distancebetween the first and second locations may be substantial (e.g., inseparate cities, states, provinces, regions, etc.). This is not,however, necessarily the case. In a clinical setting, for example, thefirst location where unit 11 is located may be in an examination roomand remote system 40 may be in a separate room in the same facility.

Remote system 40 may comprise any computer, or system or network ofcomputers, that processes, stores and/or communicates informationremotely from any of unit 11, measurement device 12 or communicationsdevice 30, as generally described herein. Remote system 40 preferablycomprises a general purpose or commercially-available server withappropriate server software and preferably known orcommercially-available database software capable of performing the tasksand functions as described herein. Remote system 40 may transmit currentinformation, previous information, information that has been acquiredsubsequent to the measurement or data of interest, or combinations ofthese.

Remote system 40 also comprises a transceiver device 44 suitable andappropriate for communications, preferably bi-directional or duplexcommunications, with communications device 30 of unit 11. Transceiver 44may comprise any of the technologies described herein above with respectto communications device 30.

Remote facility 42 may comprise a data center, health care facility suchas a hospital, clinic, or doctor's office, and/or the like. The locationof remote system 40 and remote facility 42, however, need not be fixed,and remote system 40 may be moved to a new location or locations (e.g.,from time to time). Remote system 40 also may comprise a distributednetwork. In presently preferred system designs, remote facility 42comprises a data center that comprises one or more servers (remotesystem 40) for storing, managing and inputting and outputting ordistributing data as described more fully herein below. This is not,however, limiting. Remote facility 42 may, for example, comprise a setof regional facilities, a distributed data management system withmultiple facilities, and the like. Additionally, remote system 40 mayeither be under the operational control of the user of the system (e.g.,system 10), or it may be under the control of a third-party (e.g., aservice provider.)

In presently preferred embodiments, remote facility 42 comprises anetwork service or cloud provider that services various customers, andwhich serves as a portal or repository for the facilities that also maycomprise part of the remote facility 42 (e.g., a hospital, clinic,doctor's office, and/or the like).

Remote system 40 comprises a database, for example, such as any one ofthe several commercially-available database application systems that isconfigured to store and transmit or receive data as described herein. Anexample would comprise the types of commercially-available databasesthat are used to house and manage health records.

With this system 10 as herein described, communications device 30 ofunit 11 and remote system 40 are configured to communicate data,preferably but optionally bi-directionally, preferably including themeasurement signal and additionally parameter data and population dataas described more fully herein below. This communications link,indicated by reference numeral 32, preferably is at least partiallywireless and preferably includes an Internet connection or access.

A system 110 according to another presently preferred embodiment of thepresent disclosure is shown in FIG. 2. System 110 also is configured tomeasure acetone in the breath of a user, typically in the form of abreath sample.

System 110 comprises a breath analysis or measurement device 112.Presently preferred breath acetone measurement devices are those shownand described in U.S. Provisional Patent Application No. 61/800,081,U.S. Utility patent application Ser. No. 13/052,963, and U.S. Pat. Nos.6,609,068 and 7,364,551.

Measurement device 112 comprises a breath sample input in the form of amouthpiece 116, into which the user may blow or exhale directly to inputa breath sample. It further comprises a sensing system 118 in fluidcommunication with input 116 so that the breath sample contacts orinteracts with the sensing system, whereupon sensing system 118 measuresthe concentration of the acetone in the breath sample and generates ameasurement signal representative of that acetone concentration. Thesensing system may be as described herein above.

Sensing system 118 preferably comprises a metal-oxide nanoparticle-basedsensing system, such as those described in U.S. Utility patentapplication Ser. No. 13/052,963.

System 110 also comprises an electronic or communications device 130 inoperative communication with measurement device 112. In system 110,electronic device 130 comprises a computing device with a wireless link,such as those described in U.S. Provisional Patent Application No.61/981,457, which is incorporated herein by reference. Electronic device130 includes a touch screen 134 for input of data or information andpresentation of displays. The electronic device 130 also comprises aprocessor 152 and storage 152 a. Moreover, the electronic device 130 isloaded with the App (e.g., instructions that can be executed by aprocessor that instruct the electronic device 130 to perform processesdescribed herein). Electronic device 130 includes the samecommunications equipment and features as described herein above withrespect to device 130, and is adapted to receive the measurement signalfrom measurement device 112, preferably wirelessly.

In other embodiments of the system 110, the measurement device 112includes a touch screen or other display capable of displaying aninteractive user interface. In such embodiments, some or all of the userinterface displays described herein (such as those of the App) mayinstead be provided on the measurement device 112. Further, in theseembodiments, the App may run entirely on the measurement device 112(which may communicated directly with the remote system 140), and theelectronic device 130 (e.g., smartphone) may be omitted.

Both measurement device 112 and electronic device 130 are located at afirst location which, as previously described, preferably comprises alocation at which a user of the system, such as the user whose acetonelevels are being measured is located (e.g., at the user's home orworkplace).

System 110 further comprises a remote system 140 disposed within aremote facility or center facility 142 at a second location remote fromthe first location. In system 110, remote system 140 and remote facility142 are fully equivalent to remote system 40 and remote facility 42 ofsystem 10, shown in FIG. 1 and described herein above.

With connections or communication links as described herein above withrespect to breath analysis device 112, electronic device 130 and remotesystem 140, system 110 is configured to communicate data, preferably butoptionally bi-directionally, preferably including the measurement signaland additionally parameter data as described more fully herein.

A system 210 according to still another presently preferred embodimentof the present disclosure is shown in FIGS. 3-5. System 210 also isconfigured for measuring acetone in the breath of a user. With referenceto FIG. 3, system 210 comprises a breath ketone measurement device 212,an electronic or communications device 230, which in this embodimentcomprises the same electronic device as electronic device 130 in system110 of FIG. 2, and a remote system 240 identical to remote system 140 ofsystem 110 in FIG. 2. The related components and communications links asshown in previous drawing figures and described herein may apply to thissystem as well, as indicated by the same or like reference numerals.Other than the substitution of a different acetone measurement device,system 210 actually is similar to or exactly the same as system 110 ofFIG. 2.

As shown in FIG. 4, breath acetone measurement device 212 of system 210comprises a base or base unit 212 a. Base 212 a comprises a power button212 b and an input port 216 a.

Device 212 further comprises a breath sample bag or breath bag 216 bthat includes a bag mouthpiece and ferrule 216 c and one-way valve 216d. Breath bag 216 b is a physically separate component relative to base212 a, but is adapted to detachably couple to base 212 a by couplingbreath bag ferrule 216 c into base input port 216 a so that they seal ina fluid-tight manner. This coupling preferably is confirmed by a switchthat is electrically coupled to a microprocessor.

In use, initially breath bag 216 b is detached from base 212 a and isfully deflated. The user places his or her mouth at the bag mouthpiece216 c and blows into bag 216 b to inflate it with a sample of the user'sbreath. One-way valve 216 d allows the breath sample to enter bag 216 b,but prevents the breath sample from escaping back out mouthpiece 216 c,thus retaining the breath sample in the bag.

The user then places the inflated breath bag 216 b onto base 212 a byinserting ferrule 216 c into base input port 216 a. As this matingoccurs, one-way valve 216 d is pressed onto a post 216 g disposed inbase input port 216 a, which biases a flap in the one-way valve andopens it so that the breath sample is released from the bag 216 b andcan freely enter base input port 216 a.

With reference to FIG. 5, which shows a cross-sectional cutaway sideview of base 216 a, the breath sample from breath bag 216 b, uponentering base 212 b through input port 216 a, is directed along a flowpath 218 a under the influence of a pump 218 b to the sensing system218. Sensing system 218 comprises a detachable cartridge 218 c that inturn comprises a chemical interactant 218 d that reacts with acetone inthe breath sample to cause a color or chromatic change in the cartridgethat is representative of the concentration of acetone in the breathsample. A camera 218 e that is sensitive to the colorimetric orchromatic change senses the change present in the cartridge as a resultof the breath sample exposure, and communicates that sensed change as ameasurement signal to a sensing system circuit board 218 f. Circuitboard 218 f performs routine signal conditioning and formatting on themeasurement signal and communicates it to processor 250, which stores itin storage 250 a. Processor 250 then transmits the measurement signal tocommunications device 230 using transceiver 220 (which is identical toor the equivalent of transceiver 120 in system 110).

Systems 10, 110 and 210 can be used according to presently preferredmethod implementations to measure the breath acetone levels of a user asillustrated in the following examples. It will be appreciated, however,that the methods are not necessarily limited to conduct using thesespecific systems, and that variations on those systems and indeed othersystems may be used to perform the methods.

Insertion of Detachable Components

In an embodiment, breath analysis devices, such as the breath acetonemeasurement device 212, include components, referred to herein aspresence sensors, that identify or recognize when a detachable,disposable, and/or replaceable accessory component (e.g., breath bag,cartridge, test strip, etc.) is correctly mated with the breath acetonemeasurement device. Examples of presence sensors may include bumpswitches, magnetic switches, piezoelectric sensors, proximity sensors(which may include a photodiode), software-coupled image sensors (e.g.,a camera that periodically captures an image of a region of interest andprocesses the image to determine if the detachable component iscorrectly mated and in place), and/or the like. The presence sensors mayalso include an electrically conductive material (e.g., a piece ofconductive copper tape) that is coupled to the detachable component andthat is also embedded within the base unit of the breath acetonemeasurement device such that when the electrically conductive materialand the detachable component are in physical contact with one another,they complete an electrical circuit. The presence sensors may also be aplurality of presence sensors that, alone or in combination, providemore specific guidance to a user (e.g., via a user interface on a mobileapplication, via a display on a breath acetone measurement device, etc.)on what steps or actions the user may need to perform to correctlycouple or insert a detachable component.

The breath acetone measurement devices may also use various sensorsystems to generate readings. Sensor systems include, withoutlimitation, colorimetric sensors, enzymatic and electrochemical sensors,thermal and thermoelectric sensors (e.g., thermopile sensors),nanoparticle or metal oxide-based sensors, and/or the like. Examples ofcolorimetric sensors are provided in U.S. Provisional Application No.61/800,081, which is commonly assigned to the assignee hereof and ishereby incorporated herein by reference in its entirety. Examples ofmetal oxide or nanoparticle sensors are provided in U.S. patentapplication Ser. No. 13/052,963, which is commonly assigned to theassignee hereof and is hereby incorporated herein by reference in itsentirety. Examples of electrochemical enzyme sensors are provided inU.S. Pat. No. 7,364,551, U.S. patent application Ser. No. 12/228,046,and U.S. patent application Ser. No. 13/194,564, which are commonlyassigned to the assignee hereof and are hereby incorporated by referenceherein in their entireties. Other examples of sensors are provided inU.S. Pat. Nos. 6,609,068 and 7,364,551, which are commonly assigned tothe assignee hereof and are hereby incorporated by reference herein intheir entireties.

FIG. 23 illustrates a breath acetone measurement device base unit 2300.Certain embodiments of breath acetone measurement devices utilizecolorimetric sensors, where the color-generating reagents are containedwithin detachable cartridges, such as within those cartridges disclosedin U.S. patent application Ser. No. 14/206,347, which is herebyincorporated herein by reference in its entirety.

In an embodiment, the base unit 2300 includes a cartridge insertion area2305 and a breath bag insertion area 2310. The cartridge insertion area2305 may be configured to receive a detachable cartridge. In thisembodiment, the cartridge insertion area 2305 includes a key 2315 thatmay ensure that the cartridge is oriented in a specific physicalorientation when inserted into the base unit 2300. The existence of akey may also help ensure that the user positions the cartridge correctlyinto the base unit 2300.

The cartridge insertion area 2305 may further comprise a presence sensor2325. For example, the presence sensor 2325 may be a bump switch. Thepresence sensor 2325 may be disposed such that the protruding portion ofswitch 2330 is depressed when the cartridge is pressed into position.Thus, the presence sensor 2325 may sense when the mechanical placementof the cartridge or a portion of the cartridge in the cartridgeinsertion area 2305 is such that the switch 2330 is depressed (e.g., thepresence sensor 2325 may sense when the cartridge or a portion of thecartridge fits completely or nearly completely and properly in thecartridge insertion area 2305). The processing unit of the breathacetone measurement device (not shown) may monitor the state of theswitch 2330 to determine when the switch 2330 is depressed. Likewise, ifthe switch 2330 transitions from a depressed state to an undepressedstate, the processing unit may detect that the switch 2330 isundepressed. To ensure a strong seal in the flow path of the breathsample, it may be important for the user to press the cartridge all theway into the cartridge insertion area 2305.

It may also be desirable that the presence sensor 2325 be fluidicallysealed within the cartridge insertion area 2305 such that the breathsample does not leak or seep into openings between the enclosingplastics of the breath acetone measurement device and the switch 2330. Agasket 2320 may further facilitate the fluidic sealing.

The breath bag insertion area 2310 may be configured to receive areplaceable breath bag. The base unit 2300 may include two prongs 2335that protrude into the one-way valve of the breath bag when the breathbag is inserted. A gasket 2340 may ensure that the breath sample doesnot leak or leak above a threshold value. The breath sample may bedirected substantially through hole 2345 in the breath bag insertionarea 2310. Once the breath bag is in place, bump switch 2350 may beactivated (e.g., by being depressed due to the insertion of the breathbag). Thus, the bump switch 2350 serves as a breath bag presence sensorand may sense when the mechanical placement of the breath bag or aportion of the breath bag in the breath bag insertion area 2310 is suchthat the two prongs 2335 protrude into the one-way valve of the breathbag (e.g., the bump switch 2350 may sense when the breath bag or aportion of the breath bag fits completely or nearly completely andproperly in the breath bag insertion area 2310). The bump switch 2350may also sense when the breath bag is fluidically coupled to the hole2345 (e.g., fluid may flow between the breath bag and the hole 2345 orother part of the flow path without leaking or substantially leakinginto another part of the breath acetone measurement device) based on,for example, the mechanical placement of the breath bag. The processingunit of the breath acetone measurement device may monitor the state ofthe bump switch 2350. Activation of the bump switch 2350 may cause theprocessing unit (not shown) to sense that the bump switch 2350 of thebreath acetone measurement device is active and that the breath bag isin place. Likewise, if the breath bag is initially in place, but thenslips out of place, the bump switch 2350 may be deactivated. The bumpswitch 2350 thus serves as a breath bag presence sensor. Deactivation ofthe bump switch 2350 may cause the processing unit to sense that thebump switch 2350 is deactivated and that the breath bag is not in place.

FIG. 24 illustrates a breath acetone measurement device that usesnanoparticle-based sensors. In embodiments disclosed in U.S. ProvisionalPatent Application No. 62/161,872, titled “BREATH ANALYSIS SYSTEM,DEVICE AND METHOD EMPLOYING NANOPARTICLE-BASED SENSOR” and filed on May14, 2015, which is hereby incorporated herein by reference in itsentirety, a user may attach a mouthpiece 2400 and/or a conditioningdevice (e.g., a desiccant-containing ampoule) 2405 to the inlet of abreath acetone measurement device, where the breath acetone measurementdevice includes a nanoparticle-based sensor 2410. Inserting themouthpiece correctly may activate a switch 2420, which may be monitoredby processing unit 2425 and cause the processing unit 2425 to sense thatthe switch 2420 has been activated and that the mouthpiece is correctlyinserted. Likewise, removal or a partial removal of the mouthpiece maydeactivate the switch 2420, which may cause the processing unit 2425 tosense that the switch 2420 has been deactivated and that the mouthpieceis not correctly inserted.

Similarly, inserting the conditioning device correctly may activate aswitch 2415, which may be monitored by the processing unit 2425.Activation of the switch 2415 may cause the processing unit 2425 tosense that the switch 2415 has been activated and that the conditioningdevice is correctly inserted. Likewise, removal or partial removal ofthe conditioning device may deactivate the switch 2415, which may causethe processing unit 2425 to sense that the switch 2415 has beendeactivated and that the conditioning device is not correctly inserted.

FIG. 25 illustrates a breath acetone measurement device that usesnanoparticle-based sensors and a breath bag 2500 instead of amouthpiece. The breath bag may ensure that a controlled volume of breathis exposed into the nanoparticle-based sensor. When the breath bag isinserted, the tips of the bag may press down on a bump switch 2510.Processing unit 2520 may monitor the state of the bump switch 2510.Pressing down on the bump switch 2510 may cause the processing unit 2520to sense that the bump switch 2510 is activated and that the breath bagis inserted. Likewise, if the breath bag is removed or partially removedsuch that the tip of the bag no longer presses down on the bump switch2510, the processing unit 2520 may sense that the bump switch 2510 is nolonger activated and that the breath bag is not inserted properly.

Unlike the embodiment shown in FIG. 24, conditioning device 2505 (e.g.,a desiccant) may be located further downstream from the inlet and mayalso be recognized by a switch 2515. The processing unit 2520 maymonitor the state of the switch 2515. Activation of the switch 2515 bythe conditioning device 2505 may cause the processing unit 2520 to sensethat the switch 2515 has been activated and that the conditioning device2505 is properly inserted. Likewise, removal or partial removal of theconditioning device may deactivate the switch 2515, which may cause theprocessing unit 2520 to sense that the switch 2515 has been deactivatedand that the conditioning device 2505 is not properly inserted.

Other breath acetone measurement devices may use electrochemical sensorsthat are coupled to enzyme systems. FIG. 26 is an embodiment of anintegrated mouthpiece 2615 for use in conjunction with anelectrochemical breath acetone measurement device 2600. The breathacetone measurement device 2600 may include an electrode insertion area2605 and a hydration system insertion area 2610.

In an embodiment, the integrated mouthpiece 2615 includes a mouthpiece2620 into which a user exhales. In alternative embodiments, themouthpiece 2620 is connected to a detachable and/or disposable breathbag (not shown), such as those described in conjunction with otherembodiments described herein.

The integrated mouthpiece 2615 may further include a test strip 2625 onwhich an enzyme 2630 is disposed. The test strip 2625 may furtherinclude an electrode 2650. A wicking material 2635 may be disposed belowthe test strip 2625. When a plunger 2645 is inserted into the breathacetone measurement device 2600 via the hydration system insertion area2610, hydration liquid housed within a liquid container 2640 may bereleased through the wicking material 2635. The hydration liquid maythen be exposed to the enzyme. The interaction of the enzyme, thehydration liquid, and the analyte in the breath may result in anelectrochemically active reaction.

For this type of sensor, the proper insertion of the test strip 2525 andthe plunger 2645 may be critical. Both may be coupled to a presencesensor monitored by a processing unit (not shown) of the breath acetonemeasurement device 2600. Thus, the proper insertion of the test strip2625 may cause the processing unit to sense that the test strip 2625 isproperly inserted. Likewise, an improper insertion or the removal orpartial removal of the test strip 2625 may cause the processing unit tosense that the test strip 2625 is not properly inserted.

Similarly, the proper insertion of the plunger 2645 may cause theprocessing unit to sense that the plunger 2645 is properly inserted.Likewise, an improper insertion or the removal or partial removal of theplunger 2645 may cause the processing unit to sense that the plunger2645 is not properly inserted.

Detachable Component Detection State Diagram and User Interface

As described herein, an electronic device or mobile communicationsdevice (e.g., a smartphone), such as the communication or electronicdevice 30 in FIG. 1, the electronic device 130 in FIG. 2, or theelectronic device 230 in FIG. 3, may execute a mobile application thatincludes executable program code that directs the smartphone tocommunicate with a breath acetone measurement device and/or a server,such as the remote system 40, 140, or 240. For example, a user can usethe mobile application to verify that a detachable component has beenproperly mated with the breath acetone measurement device, to start atest, and/or to view readings (e.g., test results). As used herein, themobile application may be referred to as a software application orsoftware “App.” Similarly, the remote systems 40, 140, or 240 mayexecute a network-accessible software application that includes some orall of the functionality of the software application described herein.The features of the network-accessible software application (e.g.,allowing the user to start a test, allowing a user to view readings,etc.) may be accessible by a user via the electronic device 30, 130, or230. For example, the electronic device 30, 130, or 230 may execute abrowser that the user can use to access and view a page (e.g., a contentpage) generated by the network-accessible software application, wherethe page provides the user with access to such features.

FIG. 27 illustrates a detachable component detection state diagram,according to one embodiment. As illustrated in FIG. 27, the statediagram includes an electronic device 2710, a breath acetone measurementdevice 2730, and a server 2750. The electronic device 2710 may besimilar or identical to the electronic device 130, the breath acetonemeasurement device 2730 may be similar or identical to the breathacetone measurement device 212, and the server 2750 may be similar oridentical to the remote system 40. The electronic device 2710 mayfurther include a mobile application 2715 that includes executableprogram code that directs the electronic device 2710 to communicate withthe breath acetone measurement device 2730 and/or the server 2750. Insuch embodiments, the electronic device 2710 may be a smartphone of theend user.

In an embodiment, the breath acetone measurement device 2730 isconfigured to accept a breath bag and a cartridge. However, this is notmeant to be limiting. In other embodiments involving other types ofmeasurement devices (such as a ketone measurement device that analyzesketones in a blood or urine sample, a ketone measurement device thatdetects ketones that have permeated through the skin, etc.), the userinterface may instead indicate whether another type of detachablecomponent, such as a test strip, is properly coupled and/or may onlyprovide a notification if the detachable component is improperlycoupled. Examples of disposable or detachable components and how thesecomponents interact with blood or urine measurement devices can be foundin U.S. patent application Ser. No. 14/690,756.

As described above, the breath acetone measurement device 2730 mayinclude a processing unit that monitors states of presence sensors, suchas bump switches. Such monitored states may indicate when a detachablecomponent is properly coupled or inserted and when a detachablecomponent is not properly coupled or inserted or not present. If theprocessing unit senses (e.g., by monitoring the state of a presencesensor) that the cartridge is properly inserted (e.g., that themechanical placement of the cartridge or a portion of the cartridge inthe breath acetone measurement device 2730 is such that the cartridge ora portion of the cartridge fits completely or nearly completely in anopening provided by the breath acetone measurement device 2730 for thecartridge, such as the cartridge insertion area 2305), then the breathacetone measurement device 2730 may transmit a message to the electronicdevice 2710 indicating that the cartridge is inserted or coupled (1).Likewise, if the processing unit senses that the breath bag is properlyinserted (e.g., that the mechanical placement of the breath bag or aportion of the breath bag in the breath acetone measurement device 2730is such that the breath bag or a portion of the breath bag fitscompletely or nearly completely in an opening provided by the breathacetone measurement device 2730 for the breath bag, such as the breathbag insertion area 2310), then the breath acetone measurement device2730 may transmit a message to the electronic device 2710 indicatingthat the inflated breath bag is attached or coupled (2).

In an embodiment, the breath acetone measurement device 2730communicates with the electronic device 2710 via a wireless or wiredprotocol, such as the IEEE 802.15.1 protocol (e.g., Bluetooth), the IEEE802.11.x protocol, Ethernet, and/or the like. The breath acetonemeasurement device 2730 may periodically send transmissions to theelectronic device 2710 to confirm that the cartridge is still insertedand that the breath bag is still attached. Such periodic transmissionsmay occur multiple times per second, such that the mobile applicationmonitors the states of the presence sensors substantially in real time.As described above, the processing unit may also sense if a detachablecomponent that was once properly inserted is no longer properlyinserted. If the processing unit senses that a detachable component isno longer properly inserted, a parameter corresponding to the detachablecomponent in the periodic messages may be updated to indicate that thedetachable component is no longer properly inserted. The breath acetonemeasurement device 2730 may continue transmitting the updated periodicmessage until the state of insertion of a detachable component againchanges. In other embodiments, the breath acetone measurement device2730 may only notify the electronic device 2710 of detected changes inthe state of the presence sensors.

Once the electronic device 2710 receives messages (1) and (2) in FIG. 27(indicating that a cartridge and a breath bag, respectively, areproperly coupled), the mobile application 2715 may allow a user torequest a new reading by starting a new test. However, the mobileapplication 2715 may not allow the user to request the new reading if,for example, transmission (2) is received before transmission (1) (e.g.,the detachable components are inserted out of a requested order) or if alater message is received indicating that a detachable component is nolonger properly inserted. The user may be prevented from requesting anew reading if the detachable components are inserted or coupled in anincorrect order or if a detachable component is not properly inserted;this feature reduces the likelihood that a test will be conducted usinga breath sample that is contaminated or lost. Example user interfacescreens showing the user experience during this process are shown inFIGS. 28A-28C and 29A-29B and described below. If the user requests anew reading, the electronic device 2710 may transmit a Start Readingcommand (3) to the breath acetone measurement device 2730.

As further shown in FIG. 27, the breath acetone measurement device 2730may test the contents of the breath bag (4) in response to receiving theStart Reading command. The test may result in a reading that is thentransmitted (5) to the electronic device 2710. Automatically, withoutany user interaction, the electronic device 2710 may transmit thereading (6) to the server 2750 for storage; this desirably prevents theuser from being able to selectively block some readings from beingreported to the server 2750.

FIGS. 28A-28C illustrate a user interface displayed by the electronicdevice 2710 (e.g., a user interface of the mobile application 2715) thatdepicts a status of the insertion of detachable components. Asillustrated in FIG. 28A, the user interface presents a user with a listof steps to complete to generate a new reading. In this example, theuser interface indicates steps that are being automatically performed(or that have already been performed during prior tests, such as pairingin some embodiments), steps for the user to perform, and/or which ofthese steps have been completed. The steps include the following: (1)connect with the breath acetone measurement device 2730 (e.g., via apairing procedure as described below), (2) insert a cartridge into thebreath acetone measurement device 2730, and (3) attach the inflatedbreath bag to the breath acetone measurement device 2730. Once thesesteps have been completed in order, the “start” button 7630 is enabledor displayed, enabling the user to instruct the breath acetonemeasurement device 2730 to start a new reading. For simplicity andillustrative purposes, the user interface indicates that the electronicdevice 2710 is already successfully connected with the breath acetonemeasurement device 2730.

Boxes 2810 and 2820 indicate a status of the insertion or attachment ofthe cartridge and the inflated breath bag, respectively. For example, asillustrated in FIG. 28A, neither the cartridge nor the inflated breathbag are inserted into the breath acetone measurement device 2730. Thus,the start button 2830 is shaded out, which indicates that the startbutton 2830 is disabled and that the user cannot initiate a new reading.

As illustrated in FIG. 28B, the box 2810 may include a checkmark orother symbol to indicate that the cartridge is inserted. However, thestatus of the box 2820 is unchanged, indicating that the inflated breathbag is still not attached. Because the inflated breath bag is still notattached, the start button 2830 is still shaded out.

As illustrated in FIG. 28C, both boxes 2810 and 2820 may include acheckmark or other symbol to indicate that the cartridge is inserted andthat the inflated breath bag is attached. Thus, the start button 2830 isno longer shaded out, which indicates that the start button 2830 isenabled and that the user can initiate a new reading by selecting thestart button 2830. However, the start button 2830 may once again becomedisabled if, for example, the cartridge or the inflated breath bag isremoved or partially removed such that the presence sensor no longerdetects the detachable component as being inserted.

In other embodiments, not shown, the user interface displayed by theelectronic device 2710 may not display an indication of whether thebreath bag and/or the cartridge are inserted or coupled properly to thebreath acetone measurement device 2730. Rather, the user interface mayonly display a notification if the breath bag and/or the cartridge areinserted or coupled improperly.

FIGS. 29A-29B illustrate another example in which the detachablecomponents are inserted in a different (and incorrect) order. Asillustrated in FIG. 29A, the box 2820 indicates that the inflated breathbag is attached. However, the box 2810 indicates that the cartridge isnot inserted. Thus, the start button 2830 is shaded out and disabled.

In some embodiments, an error message is displayed to indicate a reasonwhy the start button 2830 will not be enabled even if the cartridge isinserted. The user interface may also convey to the user steps tocorrect the error. For example, if a breath bag is attached while nocartridge is present (as in FIG. 29A), an error message may be displayedindicating that, because the breath bag was attached while no cartridgewas detected, the breath bag must be removed and a new breath sampleobtained. An error message may also be displayed if, for example, theuser does not insert a detachable component into the breath acetonemeasurement device 2730 (or perform some other action) within athreshold period of time. The threshold period of time may start oncethe electronic device 2710 and the breath acetone measurement device2730 have completed connecting, once a first detachable component isinserted or partially inserted into the breath acetone measurementdevice 2730, once a user indicates that he or she would like to beginperforming the steps for generating a new reading (e.g., explicitly viaan input or indirectly by accessing a certain user interface, window, orpage in the mobile application 2715, such as the user interfaceillustrated in FIG. 27), and/or the like. As one example, once acartridge and breath bag have been coupled in the proper order, themobile application 2715 may enable the start button 2830, but thereafterdisable the start button 2830 if the test is not started within athreshold amount of time (e.g., 3 minutes). The mobile application 2715may also disable the start button 2830 if the wireless connection islost before the test is started. The logic for determining whether thebreath bag and cartridge were inserted in the proper order (and/or fordetermining whether any timing constraints were satisfied) is preferablyembodied in the mobile application 2715, but may alternatively beembodied partly or wholly in the program code that runs on the breathacetone measurement device 2730.

As illustrated in FIG. 29B, the box 2810 indicates that the cartridge isinserted. However, the start button 2830 is still shaded out anddisabled despite the fact that both the cartridge and the inflatedbreath bag are inserted. The start button 2830 may still be shaded out(disabled) because the insertions occurred in an order other than theorder specified by the steps listed in the user interface.

The user interfaces with boxes 2810 and 2820 can be adapted for use withother types of devices (e.g., devices that measure ketones in blood,urine, or other body fluids other than breath, devices that measureketones that have permeated through the skin, glucose sensors, bloodanalysis sensors, etc.) in which the user needs to complete a sequenceof steps. For example, presence sensors or other types of sensors can bemonitored by a processing unit of an analysis system to determinewhether specified steps are followed. Such information can betransmitted to the electronic device for display to the user (e.g., viathe mobile application 2715). In the case of a portable blood ketonemeasurement device, presence sensor(s) may determine when a test stripthat includes an absorbed blood droplet is inserted by the user and theuser interface may indicate whether the test strip is inserted and/ormay only provide a notification if the test strip is improperlyinserted. Additionally, a sensor may be attached to the lancet so thatthe time of the blood prick is matched against the time of the stripinsertion, such as described in U.S. patent application Ser. No.14/690,756. In the case of a portable urine ketone measurement device,presence sensor(s) may determine when a test strip that includes anabsorbed urine sample is inserted by the user and the user interface mayindicate whether the test strip is inserted and/or may only provide anotification if the test strip is improperly inserted. In the case of aglucose sensor, presence sensor(s) may determine whether an electrode isinserted by the user and the user interface may indicate whether theelectrode is inserted and/or may only provide a notification if theelectrode is improperly inserted.

Additional Features of the Software Application and Related Methods

In each of the presently preferred system embodiments described hereinabove, and in accordance with preferred method implementations accordingto aspects of the present disclosure, the acetone measurement processand other process as described herein are carried out using software,including in the form of the mobile application (e.g., the softwareapplication or software “App”). (The terms “App” and “softwareapplication” are used interchangeably herein.) In system 10, thesoftware App may be executed by ketone measurement unit 11, and morespecifically by electronic or communications device 30. The software Appmay alternatively or in addition be executed by the remote system 40 andthe remote system 40 may generate a page (e.g., a content page) toprovide access to the functions of the software App. In system 110, thesoftware App may be executed by the electronic device 130 and/or theremote system 140, and in system 210, the software App may be executedby the electronic device 230 and/or the remote system 240. In general,any of the software-implemented functions described herein (e.g.,functions described herein as being performed by the App) could beimplemented on any of the disclosed devices or systems (e.g., theelectronic devices, the measurement devices, the remote systems, etc.).To illustrate these aspects of the disclosure, a method is described.The software App may include executable program code that directs theelectronic device 230 to implement the method.

The user calls or launches the App on electronic device 230, whereuponthe App's opening panel 300 is presented on touchpad display 234 asshown in FIG. 7. This opening panel 300 presents the user with the dateand time and a number of options. As shown at the bottom of panel 300,these options include a Dashboard button 302, an Activity button 304, aCharts button 306, a Store button 308 and a Settings button 310. Thecenter of the opening panel 300 prominently features two larger buttonoptions. One is a “Perform Measurement” button 312, and the other is abutton 314 entitled “Learn About Breath Monitoring.”

It may be noted that, in each of the subsequent panels as describedherein below, buttons 302, 304, 306, 308 and 310 may continue to bedisplayed at the bottom of display 234.

Selection of the Dashboard button 302 may cause the App to display theDashboard panel. Dashboard panel allows the user to begin a breathacetone test or, equivalently, measurement.

Upon selection of Activity button 304, the App displays an Activitypanel 330 as shown in FIG. 7. The Activity panel or tab 330 allows theuser to view his or her historical breath analysis results. This tabfurther allows the user to “categorize” or “tag” data or annotateresults. In the main frame of Activity panel 330 as shown in FIG. 7, theresults of the most recent breath acetone measurements are shown, usinga format of one measurement result per horizontal line. The measuremententries are positioned under category headings of “Recent Readings,”“Yesterday,” and “Monday.” Each entry includes the date and time atwhich the measurement result was obtained. The breath acetonemeasurement results in this illustrative example are provided in partsper million (ppm) of acetone in the breath sample. Tagging may alsooccur automatically by the App, such as in the event that the useridentifies computer-determinable rules (e.g., “AM test is between 7 amand 9 am” versus “PM test is between 7 pm and 9 pm”). These rules may beinputted through the App or through a companion content page accessibleover a network (e.g. a website).

Upon selection of the Charts button 306, a Charts panel 340 is presentedon display 236. Charts panel or tab 340 allows the user to view his orher historical results in one of several graphical formats. Charts panel340 offers the user a variety of charting formats for presenting breathacetone measurement results over time. The default chart in thisillustrative embodiment presents measurement results over the mostrecent week, in a connected point format. The horizontal or x-axisrepresents the day of the measurement result and the vertical or y-axisrepresents the breath acetone concentration for the respective days inunits of ppm. The y-axis alternatively or in addition may comprise unitsof a Fat Burn Number (“FBN”) value or some other user-friendly scale. Aseries of buttons 344 at the bottom of Chart panel 340 gives the userthe option of presenting breath acetone measurement results overdifferent periods of time. In this illustrative example, the options arefor one week (1 w), one month (1 m), three months (3 m), and one year (1y).

In certain instances, it is useful for a dieter to have a user-friendlyscale, as described above. Instead of reporting in units such asparts-per-million (ppm), a Fat Burn Number (FBN) value or other similarterm may be used. A FBN value may be and preferably is the same as theppm value, but with a set number of decimal points for reporting (e.g.,tenth of a unit) and no “ppm” units. As an example, a level of 2 ppmwould be a FBN value=2. Other relationships may be used and preferablyare explained in either a user manual or a professional manualassociated with the measurement device.

As an illustrative alternative to the histogram-type chart 342 shown inCharts panel 340 in FIG. 7, a time-based point plot or scatter plot 350can be displayed by the App as shown in FIG. 8. In the point plot orscatter plot 350, the horizontal or x-axis represents time, which inthis example is demarcated in days with a corresponding date (e.g., hereMonday, March 9 through Sunday, March 14). The vertical or y-axisrepresents a Fat Burn Number value as described above. This point plotor scatter plot 350 will be discussed in further detail herein below,but it may be noted here that the point plot or scatter plot 350comprises two sets of measurement results with two measurement resultsper day being displayed: (a) one for a morning or AM measurement result;and (b) another for an afternoon or PM measurement result. Eachmeasurement result is represented by a point or dot on the chart.

Under the Charts tab 340 of FIG. 7, the user may view his or her resultsaccording to “tag” or “category,” as will be explained more fully hereinbelow. Tab 340 also allows the user to select one of several algorithmsthat present the data. Examples of algorithms in this preferredimplementation include: (a) baseline subtracted from current result, (b)three day running average of results, and (c) three day running averagesubtracted from current result. Other algorithms also may be used.

Selection of the Store button 308 causes the App to display a Storepanel 360. The Store allows the user to purchase products related to thebreath measurement or analysis device, such as the detachable,replaceable, and/or disposable cartridges and detachable, replaceable,and/or disposable breath bags. It is not uncommon for the ketonemeasurement device or devices to use a replaceable or consumablecomponent in the course of its operation. In system 210 as describedherein above, for example, base 212 a uses a detachable cartridge 218 cthat functions as part of the sensing subsystem 218 to measure theconcentration of the acetone in the breath sample. Although notnecessarily limiting, presently preferred embodiments of this cartridge218 c may be used for a single breath acetone measurement, and may bereplaced with a fresh cartridge after each measurement. Similarly, aswith many generally- or commercially-available apps oncommercially-available smartphones, the user may shop an app store andbuy or otherwise acquire apps, plugins, software features and the liketo supplement those already on electronic device 230 and included in theApp. Store panel 360 includes functionality to track the user's stores(e.g., his or her inventory of cartridge, charts, features and/or thelike), and enables the user to make additional acquisitions as needed ordesired. Store panel 360, for example, optionally includes an automaticordering feature in which, when the number of breath acetone measurementresults stored in the App are at a pre-determined threshold level, theApp automatically goes online to the Store and orders a replenishmentstock.

The Store in this App displays the number of days left until suppliesare needed. This would be based, for example, on the user's lastpurchase and the user's inputted frequency of desired measurement (fromthe Settings tab).

If the device registers that there was an internal failure, a prompt isgenerated that notifies the user that he or she will receive a discountrelated to the number of failures during his or her next purchase. Thisinformation is transmitted to the Store and used to compute the price ofthe next set of disposable purchases.

Selection of the Settings button 310 causes the App to display aSettings panel 370 as shown in FIG. 7. The Settings panel or tab 370allows the user to recognize new devices (e.g., via Bluetooth or othermeans) and store individualized information (e.g., user height, age,weight, gender, frequency of desired measurement, and otheruser-specific characteristics). Under the Settings tab, a user profilemay be created. An example of the type of information that would beuseful in a user profile for weight management is shown in FIG. 9. Suchinformation may include electronic mail address, name, date of birth,current weight, AM window start, PM window start, gender, exercisefrequency, baseline breath acetone, goal weight, lifestyle, and/orexercise intensity. This information is included in the Settings tab ofthe App or in a companion content page accessible over a network that islinked to the App.

Variations in the Measurement Results

As was explained herein above, ketone concentrations are generallycorrelated with fat metabolism. In practice, however, the complexmulti-step and multi-pathway processes between fat metabolism, ketonegeneration, and ketone clearance results in many variables that canimpact this relationship. Some of those variables are physiological withrespect to the user. Others relate to the acetone measurement itself,including, for example, the device used to make the measurement, themeasurement protocols and procedures, the environmental conditions atthe time and place of measurement, and so on. Each of these variables orfactors can impact the measurement results and obscure accuracy orreliability.

In some instances, it may be best to merely discard measurement resultsthat have been adversely impacted by such variables as aberrant. Inothers, where amenable, it may be possible to compensate or adjust themeasurement results to account for the variable or variables. In manycases, however, one preferably anticipates the variable or variables andavoids them or their impact all together, or attempts to isolate theimpact of such variable or variables and separately identifies andreports the variable or variables.

An example of a variable that can adversely impact measurement resultsand a method for accommodating it according to an aspect of the presentdisclosure is found in the time of day at which the breath acetonemeasurement is made. On average, breath acetone levels when measuredfirst thing in the morning, prior to eating or drinking beverages otherthan water, are lower than acetone levels measured in the afternoon orevening. Thus, if one merely directly compares breath acetone levelsmeasured at random times throughout the day, a relatively highvariability in the measurement results will be observed. Byreconfiguring the measurement results data, (e.g., by segregatingmorning measurement results from afternoon results), that data maybecome much more comparable and useful, and may limit if not eliminatetime-of-day variability that is unrelated to true or major changes infat metabolism. This is illustrated in FIG. 8, wherein the sequentialdaily morning or “AM” measurement results are connected by a dashed line352, and the sequential daily afternoon measurement results areconnected by a solid line 354. The crossed out points may bemeasurements that either the App or the user identified as “aberrant” ornot complying with pre-set rules. As an example, consider the following:the user is told to perform breath acetone measurements between 7 am and9 am. The user, however, fails to perform a measurement within thiswindow and instead performs a measurement at 10 am. This data point isan example of an “aberrant” measurement.

Incidentally, a “baseline” horizontal line 356 and a “personal best”horizontal line 358 can be superposed on the data. These will bediscussed further herein below.

The measurement results data and variability, however, are not always asamenable to identification and modified presentation as the time-of-dayexample illustrated in FIG. 8. A more complex, and more commonlyencountered, type of measurement results data set is shown in FIG. 10.FIG. 10 presents a plot of breath acetone measurement results for a useron a program diet. The horizontal or x-axis represents time, and morespecifically the number of days the user has been on the diet. The upperportion of the vertical or y-axis represents the user's breath acetoneconcentration (in ppm) as measured using a system as depicted in FIGS.3-5 on the day corresponding to the associated position on the x-axis.The lower portion of the vertical or y-axis represents the user's bodyweight (in pounds or “lbs.”) measured at the time of acetone measurement(e.g., using a standard weight scale). The breath acetone measurementsare taken each morning, so each data point in the upper portion of theplot represents a one-day measurement.

For the first eight days, the acetone concentration is relativelyspread, but after the first three days shows a reasonably steady trendupward, indicating that fat metabolism is stable and increasing. Atabout day nine, just before the point demarcated as line A in FIG. 10,the acetone concentration makes a local peak and, for the following twodays, sharply declines in a steep downward trend. At day 11, themeasured acetone concentration returns to near its prior local peaklevels and continues in this range through day 14. On day 15, demarcatedin FIG. 10 at line B, the acetone concentration drops modestly relativeto the day 10-14 trend, but promptly begins trending upwardly, jumpingsignificantly on day 19 and remaining at this elevated level on day 20.Beginning on day 21, demarcated at line C, the acetone concentrationonce again drops substantially, and it remains at its relatively lowlevel for the following four days, returning to its previously higherlevels on day 27, and proceeding even higher on days 32-34. Beginning onday 35 and continuing for the next two days (generally demarcated atline D), the acetone concentration drops precipitously. It recovers onday 38, drops modestly on days 39-40, and again drops precipitously ondays 41-43.

Viewing this raw data without further explanation, it appears quiteerratic and inconclusive. Taken in context and with knowledge of theuser's activities, however, it provides useful information. The eventsdemarcated as lines A, C, D and E in FIG. 10 are non-compliance events,in which the user deviated from the treatment program. Line B is whenthe user saw the weight loss coach, which resulted in a temporarilypositive increase in breath acetone levels.

Baselines and Normal State

In some preferred embodiments and method implementations according toaspects of the present disclosure, it is desirable to establish aninitial reference point, condition, or set of conditions against whichactual ketone measurement results can be compared to identify orevaluate changes (e.g., over time). This initial reference point,condition or set of conditions may comprise or reflect a “baseline”(e.g., a normal state), an expected value or set of values, a selectedstarting point, and/or the like, from which ketone measurementsaccording to various aspects of the present disclosure may be measuredor compared. The baseline in general terms may be a standard value orset of values for a factor relevant to analyte measurement (includingthe measurement of multiple analytes), that is expected to change for agiven user from time to time or measurement to measurement, or for agiven user relative to a population of users (e.g., from a statisticalnorm). If a user is about to commence a new weight loss program, forexample, the user's ketone levels for a period of time immediately priorto commencing the program may serve as a baseline. As the program isundertaken and progresses, the App can compare ketone measurementresults to the baseline to provide a better understanding of thesubsequent ketone measurement results and thus to provide information onthe effectiveness of the program, the user's compliance with theprogram, and/or the like. In another example, this one involvingevaluation of a drug regimen, if one were to measure the concentrationof a particular analyte before and after administration of a drug, forexample, the baseline generally would be the analyte concentrationbefore the drug was administered. If one were to measure analyteconcentration as part of monitoring a physician-prescribed diet, thebaseline generally would be the analyte concentration just before thediet began. As an illustrative example, in FIG. 10, a baseline may beestablished by taking the average of the breath acetone measurementresults for the first seven days. This baseline, which for this data isapproximately 1.9 ppm, is shown in FIG. 10 as a horizontal dashed line356 a.

A user baseline is preferably determined by the App at a pre-selectedphysiological state. Examples of such physiological states wouldinclude, for example, fasting or not-fasting, dieting or non-dieting,with or without exercise, and others. A user baseline may be computed bythe App, for example, using an average breath acetone measurement valueobtained over a fixed or set number of measurements. As an example, theApp can average breath acetone measurement results over a fiveconsecutive day period. Depending on the particular application (e.g.,the user's lifestyle), in some instances it will be preferable toestablish the baseline under tightly controlled circumstances. For otherindividuals and in other applications, the baseline may incorporatefactors that may be expected to change for that individual and theplanned program. For instance, if the individual does not exercise everyday, but does exercise two times a week, the baseline may be taken bythe App over two weeks and encompass four workout events.

A baseline in a general sense of the term may represent a logically- orarbitrarily-designated starting point from which to judge or evaluateother measurement results, usually subsequent to the baseline. Anexample of a particular baseline, and often a relatively useful andpotentially important one, involves a “normal state” of the user.Although the “normal state” of the user typically is a relative term orfactor that can depend on various factors as well the specific analytebeing measured, it is generally understood to reflect a long termdesired state of health and wellness. A “normal” weight for a user, forexample, might be a weight that is suitable, appropriate or even idealfor a person of the user's height and frame. Although a normal state canserve as a baseline, it need not be a state or condition that the useractually was in at any particular time. It may, for example, merelyrepresent a goal or target. A “normal” state can be defined or selectedby the user or user of the ketone measurement device (e.g., via the App)or, for example, the physician prescribing the ketone measurement andoverseeing the use of the measurement or analysis device.

A “baseline” can be, but need not be, a “normal state.” In the contextof a weight management program, for example, the weight at the beginningof the program may be selected as the baseline, and the “normal state”of the user may be the target weight to be achieved during the program.Alternatively, the baseline may be the weight at the beginning of theprogram, and the target weight for the completion of the program may bewell above what might be considered a “normal” weight for that user. Aseries of programs, in that illustrative case, may be required toachieve the “normal” state.

It is also useful in some contexts and embodiments to include ademarcation for the user's “personal best.” As the name implies, thispersonal best is a measurement result or other measure that reflects thebest result or results achieved by that user in the course of theprogram or time of acetone measurement. With reference to FIG. 8, theuser's personal best acetone measurement result, measured as a Fat BurnNumber value, was a 5, and was achieved on Tuesday, March 10. Todemarcate and highlight this, the App may display a marker, such ashorizontal line 358, identified as representing the personal best. Thisline provides the user with an ongoing indication of how well theprogram objectives are being met relative to the personal best, orsimilarly, with respect to what the user is capable of achieving basedon his or her own prior results. Preferably, the “personal best” doesnot account for any measurements that were marked or identified asaberrant.

To illustrate the use of a baseline to improve breath acetonemeasurement, consider the following presently preferred methodimplementation. For simplicity and ease of illustration, this preferredmethod is described as being implemented using system 110 (e.g., theelectronic device 130 or the remote system 140) of FIG. 2 and the Appdescribed herein, although this is not necessarily limiting.

The method comprises specifying a physiological state at which breathacetone levels of the user should be determined. In this preferred butmerely illustrative method, this is accomplished by a person planningthe program, who may be the user, a treating physician, nutritionist orother clinician, and/or the like. This planner considers the user, theplanned program, the objectives of the program, etc., and selects orotherwise specifies, for example in the App, the physiological state ofthe user at which the breath acetone measurements are to take place. Asan example, given known variations in breath acetone levels when themeasurement is made first thing in the morning as opposed tomeasurements made in the afternoon, the planner may determine that twomeasurements will be made each day, one in the morning and one in theafternoon. The planner also may build into the program a specified lightlunch, and a daily exercise regime to be undertaken immediately beforelunch. Thus, the physiological states specified by the planner at whichthe breath acetone levels will be measured or determined have been setand correspond to the user's physiological states resulting from thosetwo measurement regimes. As the description of this method proceeds,focus will be on the morning measurement regime to simplify thedescription. It will be appreciated, however, that the same approach andmethod steps can be applied to both the morning and afternoon tests andassociated physiological states.

The method also comprises determining the breath acetone level of theuser at the physiological state for a period of seven days or until thebaseline is stable for 4-5 days in a row to determine a user baseline.This aspect of the method is carried out using system 110, under thecontrol of the App as described herein above, to take a morning breathacetone measurement for the user each day for five consecutive days. Themeasurement results are displayed on the scatter plot as shown withcurve 352 in FIG. 8. The App may take the average of the five breathacetone measurement results and plot that average as a baseline (e.g.,as a horizontal line 356 on the scatter plot 350).

The method then comprises determining one or more subsequent breathacetone levels, comparing the subsequent breath acetone level againstthe user baseline to generate a comparison, and outputting thecomparison. This may be carried out by having the user use system 110each morning to measure his or her breath acetone level, which value isinputted into electronic device 130 under the control of the App. TheApp then compares each of the measured values to the baseline.

The comparison to the baseline can occur in a number of different ways,including simple subtraction. For example, statistical results orrelationships, such as those presented in the legends of FIGS. 18A-E,FIG. 19, FIG. 20, and/or FIG. 21, can be displayed by the App. Thesestatistical results or relationships are described in greater detailbelow. The method may further comprise allowing the user to trackevents, and associating the events with the results.

As described herein, the App may determine a user baseline, compareketone measurement results to the baseline, display the comparison,and/or provide other functionality related to the determined userbaseline. For example, when the App is executed by the electronic device130, the App may cause the electronic device 130 to generate a userinterface that displays the comparison and/or to provide otherfunctionality described above. As another example, when the App isexecuted by the remote system 140, a user may use a browser running onthe electronic device 130 to access and view a page generated by theApp. The page may be interactive such that the user can view thecomparison and access the other functionality described above.

Reminders and Interactive Reminders

As has been noted herein above, the present inventors have discoveredthat the timeliness of ketone measurements and the faithfulness of usercompliance with certain program rules regarding breath acetonemeasurement are particularly important to the success of many programs,and that specifically pre-determined or pre-calculated reminders andinteractions with the user can greatly increase program success.Accordingly, the present disclosure according to one aspect comprisessystems, devices, and methods for measuring a ketone in the user'sbreath that comprise, inter alia, the provision of reminders, andinteractive reminders.

As explained above, in the relatively inchoate field of breath analysis,most of the data and information available on such topics as patient oruser compliance—which has been relatively sparse—have been obtained inclinical settings, where clinical staff was present and patient or usercompliance was not a significant issue. More generally, in the generalarea of diet program compliance, the traditional approach to monitoringpatient or user compliance has been for the user to record daily dietaryactivity in a journal. This approach has been highly deficient inmonitoring compliance, and has been vulnerable to intentionalnon-compliant behaviors. Moreover, these deficiencies normally arediscovered, if at all, only after the fact, when they are more difficultto correct or address.

In accordance with this aspect of the present disclosure, a method isprovided that uses a system (e.g., one of those described herein above)that includes memory that has stored therein data representing one ormore rules relating to a program (e.g., those outlined in Tables 1 and2) and a reminder set associated with the respective rule or rules. Thesystem may provide the user with at least one reminder in relation tothe rule or rules via a user interface. These features can be employedusing a software application that is operatively disposed in the system(e.g., the electronic device or the remote system as described in thepreviously-described system embodiments) to generate the at least onereminder. For example, the reminders may be presented via a mobileapplication that runs on a smartphone, tablet, smart watch, or othermobile communications device of the user.

The following example, presently in connection with use of system 110for illustrative purposes, provides a presently preferred but merelyillustrative method implementation according to this aspect of thepresent disclosure.

The App as described herein is loaded into electronic device 130 ofsystem 110, and has been launched by the user. As an example, thefollowing program rules have been selected for the user by the userand/or his or her treating nutritionist or healthcare professional:

-   -   Rule 1: Morning Breath Acetone Test: The user will test breath        acetone first thing each morning, between a specified time        (e.g., 7 am and 9 am), prior to eating any food or drinking any        beverages other than water, and before any exercise is        performed.    -   Rule 2: Breakfast: Breakfast may be eaten after the morning        breath acetone test, but no later than a specified time (e.g.,        10 am).    -   Rule 3: Prescription Medication: Given the fact that the user        takes a prescription medication daily, and in view of the        requirements and restrictions of the medication itself, the user        will take the medication after the morning breath acetone test,        but no later than a specified time (e.g., 11 am).    -   Rule 4: Resistance Exercise: Each Monday, Wednesday, and Friday,        between a specified time (e.g., 12 pm and 2 pm), the user will        engage in 20 to 30 minutes of weight or resistance exercise.    -   Rule 5: Cardiovascular Exercise: Each Tuesday and Thursday,        between a specified time (e.g., 12 pm and 2 pm), the user will        engage in 20 to 30 minutes of aerobic cardiovascular exercise.    -   Rule 6: Evening Breath Acetone Measurement: Each evening between        a specified time (e.g., 5 pm and 8 pm), the user will take        another breath acetone test or measurement. The user may not        have eaten any food or consumed any beverage other than water        for at least two hours prior to taking the test. The user also        shall not have undertaken any exercise exceeding ten minutes in        duration within one hour of taking the test.        As described above, the rules may be set by the user and/or his        or her treating healthcare professional using a browser (e.g., a        web browser) executing on an electronic device, a        network-accessible application (e.g., an application executing        on a server, such as the remote system 140), an application        running locally on the electronic device 130, and/or the like.        For example, the user and/or his or her treating healthcare        professional may use the browser to log into a content page that        allows the third party to view, create, select, and/or set        rules. The rules may be stored in memory of the electronic        device 130 and/or available via a network connection (e.g., the        rules may be stored on a server, such as the remote system 140,        and accessible by the electronic device 130). As another        example, the user and/or his or her treating healthcare        professional may use a user interface generated by the        application running locally on the electronic device 130 to        view, create, select, and/or set rules and store the rules in        the memory of the electronic device 130.

With these six rules stored in memory of the electronic device 130 (orstored remotely in the remote system 140), shortly after waking on Day xof the program, the user may launch the App on electronic device 130,whereupon opening panel 300 of the App (FIG. 7) may be displayed. Theuser may select the Perform Measurement button 312. Processing of theApp from this point forward is illustrated by the flow diagram 400 shownin FIG. 11. Based on the internal clock of electronic device 130, theApp (at 402) uses the time of day input to ascertain whether the currenttime of day is within the permissible 7 am to 9 am morning time window(Rule 1) or the 5 pm to 8 pm evening time window (Rule 6). (Likewise, ifa rule is based on location of the user, the electronic device 130 canuse an internal GPS or data from an external GPS to determine whetherthe user is in the proper location.) If not within either, the App (at404) may display a message indicating that the current time of day isnot within the morning time window or evening time window and stop.Alternately, if not within either, the App (at 404) may display themessage indicating that the current time of day is not within themorning time window or evening time window, but may allow the user tocontinue with the measurement. The measurement may be marked asnon-compliant and the measurement, along with a notice identifying thedata as non-compliant, may be stored in the App and/or transmitted bythe App to the remote system 140. The measurement, when displayed to theuser or a third party via a user interface, may be marked (e.g., as anX, such as icon 359 in FIG. 8) or annotated as non-compliant to indicatethat the measurement did not fall within the Rule 1 or Rule 6 timewindow. The measurement may also not be used when performingcomputations, such as computations based on the algorithms describedbelow with respect to FIGS. 18A-22.

If the time of day is within the morning time window, the App maypresent the user with a pre-test Rule 1 confirmation panel 406, whichasks the user to confirm compliance with each of the requirements andrestrictions of Rule 1. If the user, via the App, confirms compliancewith each of the requirements and restrictions of Rule 1, the App maylog compliance confirmations (and other related data or events) andinstruct the electronic device 130 to transmit a message to themeasurement device 112 that includes confirmation of the complianceand/or an instruction to begin the measurement. The App and/or themeasurement device 112 may also transmit the compliance confirmation tothe remote system 140 for storage. The measurement may be marked ascompliant and the measurement, along with a notice identifying the dataas compliant, may be stored in the App and/or transmitted by the App tothe remote system 140. Likewise, if the user, via the App, confirmsnon-compliance with one or more of the requirements and restrictions ofRule 1, the App may log the non-compliance confirmation(s) and anycompliance confirmations and/or transmit a message to the measurementdevice 112 that includes the non-compliance and/or complianceconfirmations. The App and/or the measurement device 112 may alsotransmit the non-compliance and/or compliance confirmations to theremote system 140 for storage.

The method implementation for the morning breath acetone test embodyingRule 1 as outlined in FIG. 11 can be improved by providing one or moreselective reminders that aid in user compliance. An example isillustrated in FIG. 12. Referring to the time line 410 at the top ofFIG. 12, the horizontal axis represents time, and the morning testwindow is shown in block form encompassing the 7 am to 9 am windowaccording to Rule 1. At a predetermined time before commencement of thewindow (e.g., 15 minutes before the 7 am commencement), electronicdevice 130, based on input from its clock, provides an audible and/orvisual notice perceptible by the user and display a first reminder 412in the form of a panel 414 providing a first reminder. The firstreminder may include a notice or reminder that the morning test windowwill commence in 15 minutes (in this example), and a reminder to theuser that he or she should not eat or drink any liquids besides waterprior to conducting the test, and that no exercise is permitted prior tothe test. The panel 414 may include a confirmation button 416 for theuser to select to confirm receipt of the notice. If confirmation button416 is selected, the App may time-tag the confirmation and record orstore information indicating that the confirmation button 416 wasselected. If the user does not select confirmation button 416 within apre-determined time (e.g., 2 minutes), the App may record and store thisnon-response.

At a predetermined time within the morning test window (e.g., one houror, equivalently, half way through), if the morning test has not beencommenced pursuant to FIG. 11, the App causes a second reminder 420 toissue, as illustrated in FIG. 12. This second reminder 420 may includean audible and/or visual notice and a second reminder panel 422. Panel422 may include a notification to the user that only one hour remains inthe morning test window and may include a reminder of the other Rule 1requirements and limitations regarding eating, drinking, and/orexercise. At the bottom of panel 422, the user is presented with a“confirmation” button 424 to confirm receipt of the second reminder, aRespond button 426 to affirmatively respond to the notice, and a “StartTest” button 428 to initiate the test. If confirmation button 424 isselected, this selection is time-stamped and stored in the memory of theelectronic device 130. If Start Test button 428 is selected, the App mayinitiate the morning acetone test as illustrated in the blocks andpanels of FIG. 11.

If Respond button 426 is selected, the App presents a Response panel430, as shown in FIG. 12. The response panel 430 may enable the user toaffirmatively respond to the reminder or notice. The response may be inthe form of a set of response options, an affirmative numericalresponse, an affirmative textual response, etc., and/or combinations ofthese. This response option may be presented in a single panel (e.g.,panel 430) or through a series of panels that allow the user to inputincreasingly detailed information. As shown illustratively in panel 430,the App presents the user in this Rule 1 example with option buttons forfood, drink, and exercise (432, 434, and 436, respectively). Associatedwith each is a button or similar graphic that enables the user to inputthe amount of food and/or beverage that has been consumed or exercisethat has been done. The App also may include one or more additionalpanels below each of the food, drink, and exercise button options thatenable the user to input additional information (e.g., the specific foodor beverage consumed, the amount, the time of consumption, etc.). TheApp may screen these inputs and determine whether the breath acetonetest should be canceled, whether the time window for the test should beextended or changed, and/or the like. The App may then indicate theresult of the screening. In addition to or as an alternative to thisuser response and screening functionality of the App, some or all of thefeatures of the App described above may be performed by remote system140. In this event, the App executed by electronic device 130, which isin communication with remote system 140, can provide the user interfacefor the communications to and from the user (e.g., the user can access acontent page generated by the remote system 140 using the electronicdevice 130).

At another predetermined time within the morning test window (e.g., 15minutes prior to the expiration of the window, which in this example,8:45 am), if the morning test has not been commenced pursuant to theblocks and panels of FIG. 11, the App may cause a third reminder 440 toissue. The third reminder 440 may include another audible and/or visualnotice and a third reminder panel 442. Panel 442 may include anotification to the user of the remaining time within the morning testwindow and again may include a reminder of the other Rule 1 requirementsand limitations regarding eating, drinking, and exercise. As with panel422, at the bottom of panel 442, the user may be presented with a“confirmation” button 444 to confirm receipt of the second reminder, aResponse button 446, and a “Start Test” button 448 to initiate the test.

Upon expiration of the morning time window (e.g., at 9:05 am, if themorning test still has not been commenced pursuant to FIG. 11), the Appmay cause a final notice 450 to issue in the form of panel 452. Thefinal notice 450 may indicate that the morning test was not performedduring the morning test window and that the time window has elapsed. Thefinal notice 450 may also provide the day and date so that the user isclearly notified of which test was missed. The final notice 450 mayrequest that the user perform a test even though the time window hasexpired. If a test is performed in response to the final notice 450, themeasurement may be marked as non-compliant in a manner as describedabove and not used in any future computations by the App or the remotesystem 140. The App may also provide this information to the userthrough the Activity panel 330 (FIG. 7).

The App could use a machine-learning algorithm to learn, based on agiven user's behaviors, what types of non-compliance events are the mostlikely (or are most likely in certain circumstances, such as whentraveling), and could modify/personalize the reminders accordingly (suchas by sending additional or preemptive reminders when the computedlikelihood of a non-compliance event is high).

Optionally, as some or all of these reminders and notices are issued,data representing the reminders and/or notices may also be automaticallyor mandatorily transmitted to remote system 140 (e.g., for storageand/or viewing by a third party). Some or all of the reminders couldalternatively (or additionally) be sent as text messages, instantmessages, or some other type of message from a server, such as theremote system 140.

In an embodiment, the App varies the timing and/or content of thereminders based on sensed conditions, such as the GPS location and/orphysical speed of the mobile device running the App or the usage ofother applications on the mobile device. For example, a reminder may bedelayed if the App senses that the user is driving, is on a phone call,or is in a location that is not well suited for testing.

Similar reminders, notices, confirmations, and/or responses may beprovided by the App for the other program rules. In a reminder prior tothe afternoon breath acetone measurement, for example, the user may bereminded of the requirements, limitations, and restrictions of Rules2-6, and confirmation sought from the user that he or she is incompliance. As with the morning test, failure to confirm therequirements, etc. prior to the test will be noted, failure to commencethe test within the evening test window will preclude the test, and soon.

Acetone or Ketone Tags

As described herein, the present inventors have discovered that certainrecurring events or conditions, referred to herein as “acetone tags,”arise during the course of a typical program that can significantly maskthe correlation of the breath acetone measurements with the underlyingintracellular fat metabolism and physiological ketone production themeasurements are intended to identify. It was also noted above that ifthese recurring factors are properly identified and accommodated, theirundesirable masking effects can be mitigated or eliminated. For thepurposes of this disclosure, the term “acetone tag” and the term “ketonetag” may be used interchangeably. In general, an acetone tag maydescribe a tag used when measuring acetone levels in breath and a ketonetag may describe a tag used when measuring ketone levels in a bodilyfluid other than breath (e.g., blood, urine, saliva, etc.) or in an areasurrounding a portion of the skin in which ketones have permeated intothe open air.

An “acetone tag” is a common or recurring activity or condition relatingto the user that may impact the user's breath acetone levels, but whichmay do so on a relatively consistent basis and is planned or expected,and that lends itself to a separate or segregated data set for theketone measurement. Acetone tags are used for things, events,conditions, etc. that impact the breath acetone measurement, but whichoccur on a regular or periodic basis, or at least not uncommonly, sothat they can be anticipated and addressed. Examples of acetone tagsinclude: (a) the time of day at which the breath acetone measurement ismade (e.g., morning or evening), (b) food or beverage intake within awindow of time prior to and/or after the breath acetone measurementtest, (c) daily or periodic intake of a specific food or beverage, (d)daily or periodic administration of prescription medication (e.g.,before or after the breath acetone measurement test), (e) daily orperiodic physical activity, such as a cardiovascular or weight bearingexercise work out (e.g., before or after the breath acetone measurementtest), (f) cortisol-impacting factors (e.g., after a good night's sleepor after disrupted sleep), (g) travel, and/or (h) stressful situation.

A particular acetone tag may apply to some or all users. Breath acetonelevels generally change for any given user or person depending on whenthe measurement is made. For example, breath acetone levels may bedifferent if the measurement is made on an empty stomach than if themeasurement is made shortly after consuming a sugar or carbohydrate-richmeal.

Not all acetone tags, however, may apply to a given user, or applyprominently or significantly to a given user, particularly in thecontext of a given weight management or DKA monitoring or preventionprogram. Some users, for example, commonly exercise daily orperiodically and others do not. Similarly, some users have sleep issuesand others do not. Therefore, the user, alone or together with a thirdparty, such as a third party support person, typically identifiesacetone tags that are expected to apply in a significant way to the userand the selected program. Stated differently, the user and/or the thirdparty selects acetone tags that they wish to address during the courseof the program, based on whatever criteria they believe warrants itsinclusion.

Once the user and/or third party have selected the one or more acetonetags that are to be addressed, data representing these acetone tags arestored in the breath ketone measurement system. This may be accomplishedin a number of ways. With respect to presently preferred systems 10,110, and 210, data identifying the acetone tags may be stored in theelectronic or communications device (e.g., in the memory of theelectronic device 130 of system 110 (FIG. 2)) or in the remote system,preferably using a software application such as the App described hereinabove.

To provide a presently preferred but merely illustrative example, system110 can be used to demonstrate the storage and use of acetone tagsaccording to this aspect of the present disclosure. It will berecognized, of course, that system 110 is merely an example for purposesof illustrating systems and methods according to this aspect of thepresent disclosure, but is not necessarily limiting.

The user is presumed to be at a first location with breath ketonemeasurement device 112 and electronic device 130, the latter of which isloaded with the App as previously described herein and as furtherdescribed herein below. The acetone tags preferably are entered by theuser or a third party support person into electronic device 130 via anoption on the Charts panel 340 or the Settings panel 360 (FIG. 7).Alternatively, the acetone tags may be entered by the user on a contentpage generated by the remote system that is associated with the user andthat is accessible over a network. If the user enters the acetone tagson the content page, the remote system may transmit the entered acetonetags to the electronic device 130 via the network. In this illustrativeexample, the Settings panel 360 is used. With reference to FIG. 7, theuser launches the App, which presents opening panel 300. The user thenselects Settings button 310 at the bottom of the opening panel 300,which causes the Settings panel 370 to open. One of the options onSettings panel 360 is an Acetone Tag Selection tab (not shown). Uponselection of that tab, an Acetone Tag Selection panel 500 as shown inFIG. 13 is presented. Acetone Tag Selection panel 500 provides several(e.g., nine) pre-stored options for available acetone tags: an AM/PMTesting button 502, a Food w/in 2 Hours button 504, a Carbs w/in 2 hoursbutton 506, a Medication w/in 4 hours button 508, an Exercise w/in 2hours button 510, a High Stress button 512, a Sleep Disruption button514, a Travel button 516, and an Other button 518. AM/PM Testing button502 allows the user to select morning versus afternoon testing as anacetone tag. When this button 502 is selected, the App may segregateketone measurements made in AM hours from those made in PM hours. Foodw/in 2 Hours button 504 permits the user to select as an acetone tagwhether or not the user has eaten food within the two hours preceding aketone test. Similarly, Carbs w/in 2 hours button 506 permits the userto select as an acetone tag whether the user has eaten carbohydrateswithin the two hours prior to a ketone measurement. Medication w/in 4hours button 508 allows the user to select as an acetone tag whether heor she has administered medication within the four hours preceding aketone test. Exercise w/in 2 hours button 510 allows the user to selectas an acetone tag whether the user has engaged in exercise in the twohours preceding the ketone measurement. High Stress button 512 allowsthe user to select as an acetone tag whether or not the user has been ina high stress condition prior to the ketone test. Sleep Disruptionbutton 514 permits the user to select as an acetone tag whether he orshe has experienced sleep disruption during the night preceding a ketonetest. Travel button 516 allows the user to select as an acetone tagwhether the user has been traveling immediately prior to or at the timeof the ketone test. Finally in this example, Other button 518 allows theuser to designate a general acetone tag or to designate an acetone tagor category different from those presented in FIG. 13. Upon selectingany one of these buttons, the App may present further panels and/orfurther options for more specifically identifying or designating therespective acetone tags. At various times (e.g., when setting up a newweight management or DKA monitoring or prevention program), the user,alone or with a third party support person, may review panel 500 andmake the appropriate selection or selections from the available acetonetag options and select the Enter button 520, whereupon the App savesthese selections and returns to opening panel 300.

In the example illustrated in FIG. 13, the user selects three acetonetags from the available options (AM/PM Testing button 502, Exercise w/in2 hours button 510, and High Stress button 512), as indicated by thecheck marks in FIG. 13.

It should be noted that there are other ways to enter or select acetonetags, and the foregoing example is illustrative but not necessarilylimiting. Acetone tags also may be entered at remote system 140, forexample, in a manner as described for their entry into electronic device130, and downloaded to electronic device 130 for use by the App asfurther described herein below.

After the acetone tag or tags have been selected, entered, and stored,the system may be ready to be used in its normal operation to measurebreath acetone levels in the user's breath samples, for example, insupport of a weight management or DKA monitoring or prevention program,albeit while employing the acetone tags features. This operation willnow be described as a continuation of the use of system 110 and thispreferred method implementation.

With reference to FIGS. 2 and 11, the user launches the App onelectronic device 130 and, upon presentation of opening panel 300,selects Perform Measurement button 312. As shown in FIG. 11, the Appreceives the time of day from the electronic device clock, andautomatically registers that the ketone measurement (at 402) is an AMtest (or a PM test) for purposes of the AM/PM acetone tag. The App then(at 530 a in FIG. 11) may present an Acetone Tags panel 531 (FIG. 14),which lists the Exercise w/in 2 hours 532 and High Stress 534 acetonetags and for each provides a Yes button (532 a, 534 a) and a No button(532 b, 534 b). For each such acetone tag, the user may select the Yesor No button, where Yes indicates that the user has engaged in theacetone tag or that the acetone tag otherwise applies, and where Noindicates that the user has not engaged in the acetone tag or that theacetone tag otherwise does not apply. In an embodiment, only the acetonetags selected by or for a particular user are presented as options atmeasurement time (e.g., after the Perform Measurement button 312 isselected) in the Acetone Tags panel 531. Thus, the burden placed on theuser in categorizing the measurements may be minimized as the user maynot have to select a state for each possible acetone tag. This may alsoreduce the storage burden on the App, the measurement device 112, and/orthe remote server 140 because only the states associated with theacetone tags selected by or for the particular user would be stored. Bypresenting as options only the acetone tags selected by or for theparticular user, the likelihood of capturing the most relevant activityor condition information for the particular user may be significantlyincreased.

The acetone tags as described herein above are such that they generallycan be presumed to take one of only two possible states (e.g., yes orno). This is not, however, limiting. More than two states are possible.Moreover, even where there are only two states, they need not be simplyyes or no, and may assume other forms (e.g., high and low, 0 or 1, andso on). Accordingly, for a given acetone tag, the selection of the stateof the acetone tag may vary depending on the possible states. Thesevarious states or options for an acetone tag are referred to as an“acetone tag state.” In the preceding illustrations, for example, a“Yes” is an acetone tag state, as is a “No.”

Upon selection of the Yes and/or No buttons for the acetone tags, theApp may then proceed to the Input Breath Sample panel 540 as shown inFIG. 11. The user may input the breath sample in system 110 by exhalinginto mouthpiece 116 and selecting the Ready button 542 on touch screen134, whereupon measurement device 112 measures the breath acetoneconcentration, generates a measurement signal indicative of that acetoneconcentration, and communicates it to electronic device 130 andcorrespondingly to the App. During the operations performed by themeasurement device 112, the App may display the “Breath Acetone TestUnder Way” message (at 544). Optionally, this measurement signal may beautomatically and mandatorily transferred in an uplink transmission toremote system 140 (at 546). The measurement result may also be displayedto the user in a user interface generated by the electronic device 130(or the measurement device 112 in alternate embodiments) (at 548).

As the user continues with the program, this process may be repeated sothat a plurality of the user's breath samples are measured sequentiallyover time and a corresponding plurality of ketone measurement resultsare obtained and stored in electronic device 130 and/or remote system140. Each of the breath acetone measurement results as stored mayinclude a reference or identification number, a date and time, acorresponding indication of AM versus PM testing, and/or an indicationof the acetone tag state for each applicable acetone tag.

Upon displaying the measurement data, the App—automatically or under thecontrol of the user—can display the data segregated by acetone tagstates or categories. An example is provided by the data plot 550 inFIG. 15. As in FIG. 8, the horizontal or x-axis represents the day ofthe program and the vertical or y-axis represents the measured acetoneconcentration. Four curves or plots (552, 554, 556, and 558) arepresented. The bottom curve, designated as “AM1” (552) representsmorning measurements made with no exercise for the two hours prior tothe tests. The “AM2” curve (554) also represents morning measurements,but with exercise within the two-hour period preceding measurement.Similarly, the “PM1” curve (556) represents afternoon measurements madewith no exercise during the two hours prior to the tests, and the “PM2”curve (558) represents afternoon measurements, but with exercise withinthe two-hour period preceding measurement. Given that, in this example,the acetone tag of “high stress” also has been recorded, a similar graphto that of FIG. 15 could easily be generated from the data, or theadditional curves associated with the stress data could be overlain onthe curves in FIG. 15. The curves in the graph may be color-coded orvisually marked in some other way and displayed with a key thatassociates each color or visual marker with the corresponding acetonetag such that the curves associated with the different acetone tags areeasily identifiable.

Trigger Points

The present inventors also have discovered, as described herein, thatuser-specific “trigger points” often create unduly high risks of programnon-compliance. If these trigger points are identified and managed, theprobability and extent of program success can be greatly increased.

Based in part on user interviews and the present inventor's extensiveresearch and development in the field, it has been discovered that mostusers are able to identify a relatively small number of unplannedactivities, events or things, referred to herein as “trigger points,”that most commonly cause the user to break these rules or engage indisfavored actions.

A “trigger point,” in the context of weight management applications, isan event, activity, behavior, or item that may trigger or cause behaviorthat is adverse with respect to a weight management objective of theuser. The trigger point may or may not be planned or expected, but theundesirable or disfavored response by the user is unplanned andgenerally unexpected. Examples of events that may represent a triggerpoint include festive events such as a party, anniversary, weekends,travel, and/or the like.

Examples of activities that may represent a trigger point includestressful tasks (e.g., a difficult work task, giving a publicpresentation, attending a job interview, watching an action movie orsporting event, entertaining with friends, night eating, and so on).Examples of items that may represent a trigger point include certaintypes of foods (e.g., high caloric, sugary or otherwise unhealthy foods,alcoholic beverages, tobacco, snacks), certain medications (e.g.,medications that promote hunger, pain, stressful mental states, such asdepression, loneliness, and anxiety, and so on), and/or the like.Examples of adverse behaviors that a trigger point may threaten includeeating prohibited or disfavored foods, eating at prohibited ordisfavored times (e.g., at night), drinking prohibited or disfavoredbeverages, drinking at prohibited or disfavored times, eating ordrinking excessive amounts, and so on.

A “trigger point state” may be a state or condition that is oftenassociated with a particular trigger point. An example of a triggerpoint state may be in the morning on the way to work (the state), whichgives rise to the trigger point of drinking sugar-laden coffee.

A trigger point, in the context of weight management, may be thought ofin more colloquial terms as something that presents a high-risktemptation to a specific user to “cheat” on a weight management or DKAmonitoring or prevention program or violate the rules of that program. Agood example, although certainly among many, would be an event such as aparty, at which the various participants are drinking high-caloriebeverages, eating high-caloric or sugar or carbohydrate-rich foods. Inthese surroundings and under these circumstances, even a user that hasreasonably good discipline might be tempted to consume thosehigh-calorie foods and beverages in violation of the user's dietaryrules.

Somewhat in contrast to acetone tags, the occurrence of which generallyare fairly foreseeable and predictable, the identification of specifictrigger points often may not be difficult but their occurrence (e.g.,specifically when the user will encounter a trigger point) may berelatively unpredictable, at least in the context of planning a program.

The trigger point feature described herein may be applicable to othertypes of programs that are susceptible to non-compliance events. Forexample, such programs may include exercise regimens and certain typesof diets that do not seek to achieve weight loss.

To illustrate the effect of these trigger points, consider the breathacetone measurement results data shown in FIG. 16. The events demarcatedwith “A” and “B” are both non-compliance events. Event “A” was centeredaround a holiday and event “B” was the user's birthday. Between the timeperiods of “A” and “B,” the user was showing an increase in breathacetone levels, which may suggest that the treatment program at thosepoints was effective. When the user's trigger points of a holiday andbirthday were encountered, though, non-compliance and setback resulted.Thus, by clearly identifying and managing these trigger point events,the user can benefit from decreasing the frequency of thesenon-compliance events.

Accordingly, in preferred method implementations according to thisaspect of the present disclosure, data identifying one or more triggerpoints and associated ketone states may be stored in memory of anelectronic device or in the remote system. For example, the user or athird party may enter one or more trigger points via the App executed bythe electronic device. As another example, the user or a third party mayenter one or more trigger points on a content page generated by theremote system that is associated with a particular user. If the triggerpoints are entered on the content page, the trigger points may betransmitted to the electronic device by the remote system over anetwork. A user may then use a portable measurement device to analyzeone or more of the user's breath samples and to obtain correspondingketone measurement results. An application (e.g., the App) executed bythe electronic device, the portable measurement device, or the remotesystem may then process the ketone measurement results and the ketonestate to determine whether the trigger point occurred.

A “ketone state” may comprise an acetone concentration level, a set ofacetone concentration levels, a pattern of such levels or data points,and/or the like, that are associated with the trigger point. Forexample, if the trigger point involves eating rich foods at a party, theketone state associated with that trigger point may comprise a patternof acetone concentration measurement results that show a drop in acetoneconcentration levels. The ketone state may be determined based on aprior analysis of acetone concentration levels when a known triggerpoint occurred.

The processing of the ketone measurement results and the ketone statemay comprise comparing the acetone or ketone measurement results againstthe ketone state. For example, the values of the acetone or ketonemeasurement results may be compared with the pattern identified by theketone state to see if the values match or closely match the pattern(e.g., within a threshold value). If there is a match or a substantiallyclose match (e.g., a match within a threshold value or percentage), themeasurement device or the electronic device may determine that thetrigger point occurred. The measurement device or the electronic devicecan report this determination to the user and/or to a remote system. Themeasurement device or the electronic device can also generate an inquiryto be presented to the user such that the user can confirm or explainthe determination, and so on.

The following is one example of how the above-described trigger pointsfeature may be set up and used in the context of a weight loss program.Initially, a weight loss coach interviews a program participant and usesa web-based or other portal (which may be hosted by the remote system)to set up an account profile for the participant. During this process,the weight loss coach identifies a limited number of trigger points(e.g., 3 or 4) associated with the participant, and specifies thesetrigger points via the portal (such as by selecting from a predefinedlist of possible trigger points). In the example shown in FIG. 17, threesuch trigger points are specified: “drink soda,” “eat chocolate” and“drink coffee.” The weight loss coach may also be able to associate eachsuch trigger point with a corresponding trigger point state (e.g., “atwork”), as shown in FIG. 17. The trigger point states may, for example,specify when, where, and/or under what conditions the trigger pointstypically occur for the participant.

The program participant would also be provided with the portablemeasurement device, and with instructions for downloading the associatedmobile application to the participant's smartphone or other mobilecommunications device (tablet, smart watch, etc.). Upon installing themobile application and completing the pairing process, the mobileapplication would connect to the remote system (40, 140, 240) andretrieve configuration information, including the trigger point dataentered by the weight loss coach and/or acetone tags and/or rules thatmay also be stored in the remote system (40, 140, 240). Thereafter,whenever the participant uses the portable measurement device to take aketone measurement, the mobile application prompts the participant toindicate whether any of the designated trigger points apply to themeasurement. For example, the mobile application may ask the user toindicate which of these trigger points have occurred “since the lastmeasurement” or “in the last six hours.” The mobile application reportsthese trigger point occurrences to the remote system together with theassociated measurements.

The data (measurements and trigger point events) captured through thisprocess may be analyzed by the mobile application, the remote system,and/or a separate system component to search for correlations betweenparticular trigger points and acetone levels. For example, once thesystem has logged a threshold number of occurrences (e.g., 3, 4 or 5) ofa given trigger point (e.g., “eat chocolate”) together with associatedketone measurements, the system may use an appropriate correlationalgorithm to determine whether a statistically significant correlationexists. This analysis may, for example, reveal that the participant'sacetone level drops by 15% to 25% when the trigger point occurs. Thislearning process may continue as subsequent occurrences of the triggerpoint are recorded, such that the correlation is refined over time.

The correlations detected through this process may be used by the systemto generate appropriate messaging to the participant and/or others. Forexample, the system (e.g., the mobile application or the remote system)may detect, based on the most recent measurements, that a 20% drop hasoccurred, and that this drop correlates with the participant's “eachchocolate” trigger point.

Other factors associated with the latest measurement, such as the time,day of week, or location, may also be considered to assess whether thetrigger point has likely occurred. For example, if a trigger pointtypically occurs at a specific time or location or under specificconditions, trigger point state information can be used to assesswhether the trigger point has likely occurred. The trigger point statesmay be determined using apparatuses that are able to determine theparticipant's state without involving the participant. For example, aclock can determine if the trigger point was logged at 2 pm (while theparticipant is at work) versus 8 pm (while the participant is at home).In some cases, a participant may be in one of several locations at agiven time. A location sensor (e.g., a GPS) can determine if the triggerpoint was logged at the gym (at 6 pm) versus at home (at 6 pm).Moreover, if, for example, the trigger point “drink coffee” isassociated with the trigger point state “at night before bed,” thesystem may exclude this particular trigger point from considerationunless the latest measurement was taken between 7 PM and 1 AM.Furthermore, as described below, a biofeedback monitor such as a heartrate monitor or blood pressure meter can determine if the trigger pointoccurred when the user had recently experienced stress (due to a spikein heart rate, for instance). Trigger point states may further aid thesoftware application by causing the software application to either (a)directly log that the trigger point occurred; or (b) provide theparticipant with a more accurate proposed explanation for a change inketone measurements, which the participant may then confirm, change orignore (e.g., the detected occurrence of a trigger point may cause thesoftware application to modify the values of one or more ketonemeasurements, possibly pending participant approval).

If the system determines that a particular trigger point has likelyoccurred, it may prompt the participant (e.g., via the mobileapplication) to indicate whether this particular trigger point hasrecently occurred, may notify the weight loss coach of the possibleoccurrence, and/or revise the values of past ketone measurements. Thesystem may determine that a particular trigger point has likely occurredif, for example, the system determines that a location (e.g., detectedby a location sensor, such as a GPS) or condition (e.g., heart ratedetected by a heart rate monitor, blood pressure detected by a bloodpressure monitor, etc.) of the participant when a ketone measurement wastaken (referred to as a ketone measurement state) matches a triggerpoint state, thereby indicating that a trigger point associated with thetrigger point state may have occurred.

The process by which the trigger points feature is set up and used maydiffer for other types of programs. For example, the system may be usedby a marathon runner to monitor progress in training for a marathon. Insuch cases, the runner (rather than a coach) may directly enter thetrigger points, and these trigger points may represent particular typesof training-related events (e.g., missed day of training, intervaltraining, etc.).

As noted above, the present inventors have discovered that, in manycases, a given user is particularly tempted only by a relatively limitedset of trigger points, and those trigger points tend to be highlyuser-specific. Thus, an initial step or task may involve identifyingtrigger points that are significant to the particular user and that maypose a particularly high risk of non-compliance for the user. Theselection of such trigger points may involve subjective decision makingand discretion may be used in identifying which trigger points are to beused, how many, and so on.

In related method implementations, a user can establish a set of triggerpoints and corresponding breath acetone levels or ketone states. Theuser may then use the measurement device to take periodic ketonemeasurements (e.g., as prescribed in the applicable weight management orDKA monitoring or prevention program). A software application such asthe App described herein may monitor the ketone measurements. If one ormore ketone measurement results are in or at a trigger point level ormatch or closely match a ketone state, the system under the control ofthe App (e.g., the electronic device or the remote system) may undertakea response. That response may comprise a notice to the user that atrigger point is suspected (e.g., simple notice, encouragement, warning,etc.). The response may alternatively or in addition comprise aninquiry, a question (e.g., a question like “Has the trigger pointoccurred?” that the App presents to the user), an alert, or anotherprompt directed at the user that may request the user to interact withthe system to provide a reply to the response. The response also maycomprise a notice to a third party that a trigger point is suspected.Examples of such third parties may include a friend or family member, agroup support member, a clinical treatment monitor or advisor (e.g.,treating physician, nurse, nutritionist, etc.), and/or the like.

FIG. 17 illustrates a specific implementation of the method describedherein. In this implementation, Step 1 involves receiving indications ofthree user-specific trigger points. The indications of the user-specifictrigger points may be received, for example, via an interactive userinterface of the App or via a content page generated by the remotesystem (or via a user interface of the breath analysis device inalternative embodiments). These trigger points may be identified, forexample, by a healthcare provider after consultation with the specificuser, or they may be identified by the user, or they may be deduced fromevaluation of historical diet or exercise journals. In this example,each trigger point may be associated with a trigger point state, whichis associated with a time of day (e.g., a time when the user is at work,a time at night before the user goes to bed, etc.). The user-specifictrigger points are preferably selected from a predefined list presentedby the user interface; however, the system may also support the abilityfor a user to define a new trigger point.

According to Step 2 of this example, the breath acetone levels may bemonitored using a breath analysis device and/or software application. Abutton to begin the breath acetone test may be identified as “PerformMeasurement.” Above the “Perform Measurement” button are three custombuttons based on the user-specific trigger points (e.g., in this casethe three trigger points described above).

The user may utilize this illustrative software application for apattern recognition time period. During the pattern recognition timeperiod, the application may identify one or more patterns in breathacetone levels that represent aberrant behavior. For example, the useror a third party may log the occurrences of trigger points during thispattern recognition time period. The breath acetone measurementsassociated with the occurrence of a specific trigger point (e.g., one ormore breath acetone measurements that immediately preceded andimmediately succeeded the occurrence of the specific trigger point) maybe used to identify a pattern of breath acetone levels that result whenthe specific trigger point occurs. The pattern may be identified bycomparing breath acetone levels that resulted each time the specifictrigger point occurred (if the specific trigger point occurs multipletimes during the pattern recognition time period).

After the pattern recognition time period, the application may useidentified patterns to help the user log events that the user may haveinadvertently forgotten to record (or intentionally omitted). Becausethe identified patterns may be associated with known trigger points,specific prompts can be presented to the user. An example promptdisplayed to the user may state “This drop in breath acetone levelslooks similar to a drop seen during your night eating of chocolate. Didyou forget to log this?” as illustrated in FIG. 17. Preferred methodsinvolve logging user-specific trigger points.

User-specific trigger points may be specific to the individual. Thesetrigger points may change during the course of the individual's weightloss or weight management program. For instance, user-specific triggerpoints during the “initiation” phase of a diet may involve foods thatcause cravings to the user. On the other hand, user-specific triggerpoints during the “maintenance” phase may be extended vacations wherefriends are overeating.

User-specific trigger points may further be associated with triggerpoint states. Trigger point states may be, for example, location and/ortime of day. A specific example might be that an individual gets coffeewith sugar and cream every morning on the individual's way to work. Inthis example, the trigger point (e.g., consumption of sugar-ladencoffee) is associated with a trigger point state (e.g., morning, on theway to work).

The notion of trigger-point monitoring may allow the user to focus ondocumenting limited data. Trigger-point monitoring may also be the mostcritical data to facilitate behavioral changes. In this way,trigger-point monitoring is a potent treatment tool and reflects aparadigm shift from current thinking.

Another preferred method for this aspect of the present disclosureinvolves establishing a pattern associating certain user-specifictrigger points with breath acetone levels. Once the pattern has beenestablished, the pattern may be useful to then monitor subsequent breathacetone levels on a regular basis. The pattern and the breath acetonelevels may be used to generate a prediction regarding whether theuser-specific trigger point occurred. The prediction may be reported tothe user (e.g., via a user interface generated by the electronic deviceor via a content page generated by the remote system and accessible overa network) so that the user may confirm or clarify the prediction.

The term “on a regular basis” as it pertains to monitoring breathacetone levels is not meant to be narrowly construed (e.g., to require afixed repeated routine). A regular basis may be cyclical (e.g., everymorning, three times a week, and/or the like). A regular basis may alsobe centered around non-cyclical events. For instance, a regular basismay include the monitoring of breath acetone levels 15 days before andafter some or all family vacations.

The above method involves correlating occurrences of user-specifictrigger points with breath acetone levels to determine or establish apattern of breath acetone levels associated with the user-specifictrigger point. This may be achieved by receiving a selection or otherindication of user-specific trigger points and enabling the user toindicate when any one of the user-specific trigger points occurs (e.g.,by prompting the user when acetone measurements are taken) during apattern recognition time period during which breath acetone measurementsare received from a breath acetone measurement device. Times duringwhich the user indicates that a user-specific trigger point has occurredmay be stored. At a later time, breath acetone measurements associatedwith a time near the time during which a user-specific trigger point hasoccurred (e.g., within 24 hours of a time during which a user-specifictrigger point has occurred) and/or a threshold number of breath acetonemeasurements that occurred around the same time as when a user-specifictrigger point occurred (e.g., the 2 breath acetone measurements thatimmediately preceded the time that the user-specific trigger pointoccurred and the 2 breath acetone measurements that immediatelysucceeded the time that the user-specific trigger point occurred) may beused to determine a pattern of breath acetone levels that are stored asidentifying an occurrence of the user-specific trigger point. Forexample, the pattern may be determined based on the percent changebetween the values of the breath acetone levels before the user-specifictrigger point occurred and the values of the breath acetone levels afterthe user-specific trigger point occurred. As another example, thepattern may be determined based on a comparison of the change in valuesof breath acetone levels each time the user-specific trigger pointoccurs (if the user-specific trigger point occurs multiple times). Ifthe change in values before and after the occurrence of a trigger pointis a decrease of 10% (e.g., the first time the trigger point occurs),12% (e.g., the second time the trigger point occurs), and 14% (e.g., thethird time the trigger point occurs), for example, a combination ofthese values may be taken to determine the pattern (e.g., a mean of thepercent changes could be taken such that the trigger point is identifiedin subsequent breath acetone measurements when a breath acetone leveldrops 12%, a range of the percent changes could be taken such that thetrigger point is identified in subsequent breath acetone measurementswhen a breath acetone level drops between 10%-14%, etc.).

A “pattern recognition time period” may be a period of time sufficientto enable determination of a relationship between the breath acetonelevels and one or more of the user-specific trigger points. This periodmay be different from user to user. Moreover, this period may depend onhow frequently a trigger point occurs for the given user. For example,the App may wait for a minimum number of trigger points to occur (e.g.,3, 4, 5, etc.) before attempting to determine a pattern of breathacetone levels associated with the specific trigger point. In someembodiments, the pattern recognition time period is an indefinite periodof time because the App may continue to determine new patterns or updateexisting, recognized patterns as breath acetone measurements are taken.

Additional examples of how the pattern may be determined during thepattern recognition time period and whether, based on determinedpatterns, current ketone measurements may cause the App to identify thata trigger point occurred are described below. Like many chemicalmeasurands, ketone levels in the body are subject to variance. It is notatypical, for instance, for an individual at a physiological “steadystate” to have ketone levels that vary from time to time. A significantchange in ketone measurements, such as those associated with aclinically significant trigger point, may be determined by comparingsome characteristic of the post-trigger point levels to somecharacteristic of the pre-trigger point (e.g., steady state) levels.Examples of characteristics include: mean, median, standard deviation, amultiplier of the standard deviation, coefficient of variance, and/orthe like and may also involve combinations of these characteristics.

When comparing these characteristics, simple subtraction may be used ora more complex comparison, such as those used in determining signal tonoise ratios, may be performed. In effect, the post-trigger point stateis the “signal” and the pre-trigger point state is the “noise.” Using anappropriate algorithm (examples of which are presented in thisdisclosure), the signal and noise are distinguished.

Example 1

An example of steady state variance in breath ketone measurements for ahealthy individual who is not dieting or exercising is shown in Table 3.

TABLE 3 Day Breath Acetone (ppm) 1 0.2 2 0.4 3 0 4 0.5 5 0.3 6 1.3 7 0.3Mean 0.43 St. Dev. 0.42

In this example, this individual's steady state breath ketonemeasurements over the course of a week have an average of 0.43 ppm and astandard deviation of 0.42 ppm. Now consider the situation in which theDay 8 level is 0 ppm. In this example, despite the fact that the Day 8level is less than the Day 7 level, because the difference is less thanthe standard deviation, this difference is not considered significant.Thus, the software application may not prompt the user to confirm ordeny that a trigger point occurred between the ketone measurement takenon Day 7 and on Day 8.

In this example, the computation of the standard deviation was done over7 days, but in other instances, a shorter or longer duration may beused.

Example 2

The relationship between trigger points and ketone measurements may andoften are specific to an individual or to a class of individuals. Inother words, not all individuals respond in the same way physiologicallyto macronutrients, micronutrients, activities or situations. An exampleof a non-universal trigger point is artificial sweeteners. Exemplarydata is presented in Table 4. In this example, the user consumedartificial sweetener between the fifth measurement (M5) and the sixthmeasurement (M6).

In Table 4, the first column is the measurement number (M#, where #represents the sequential ordering of the measurements taken by theketone measurement device). The second column shows the output of theketone measurement device. The third column shows whether or not thesoftware application prompted the user to confirm or deny that a triggerpoint occurred. The fourth column presents exemplary rules that enablethe software application that determined whether or not the softwareapplication should prompt the user, as shown in column three.

TABLE 4 Output of the Ketone Measurement Rationale for Not Device,Prompting for Trigger Breath Acetone Trigger Point Points (M =Measurement (ppm) Prompted? measurement) M1 6 No First measurement M2 8No M2 > M1 M3 10 No M3 > M2 and M3 > M1 M4 5 No (M4) > (at least onegood point minus device precision) M5 7 No [Average (M1 to M4) − Stdev(M1 to M4)] < M5 < [Average (M1 to M4) + Stdev (M1 to M4)] M6 2 Yes — M74 No M7 > M6

Exemplary Algorithm M2>M1.

As shown in the row for M2, if a measurement is greater than thepreceding measurement, generally the software application does notprompt the user to confirm. There are exceptions, however, such as ifthe user is failing to recover from an already confirmed trigger pointevent within a known period of time. For example, if the user is able torecover from administration of an artificial sweetener within 3 days andhas not recovered by Day 4, the software application may prompt the userto confirm that subsequent episodes of the same or a different triggerpoint have occurred. This is not atypical in the event of a major“cheating episode” where a user begins to spiral downward and consumeincreasing amounts of a trigger point. Another situation in which anindividual may not recover within a regular recovery period is athleticinjury. An athlete may sprain his or her ankle (a first trigger point)and it is known that recovery takes 2 days. However, as a result of thesprained ankle, the athlete stops working out for those 2 days, but nowhas two hours of free time in the evening, which is spent at socialactivities where desserts (a second trigger point) are consumed. As aresult, even though the ankle may heal within two days, the ketonemeasurements do not recover by Day 3, which causes the softwareapplication to prompt for a second trigger point.

Exemplary Algorithm M4> (at Least One Good Point—Device Precision).

In addition to the inherent physiological fluctuation of the ketonemeasurements, the ketone measurement device has its own precision andvariance. Consider the situation in which the device precision is 2 ppm.Based on the preceding rule, M4<M3 and this difference appearssignificant (5 ppm). However, M4 (5 ppm) is greater than one of thepreviously recognized “good” points (M1=6 ppm) minus the deviceprecision (2 ppm). In this situation, the software application does notprompt the user to confirm that a trigger point occurred between Day 3and Day 4. Exceptions might be if the previous “good” point was too farback in time or otherwise not applicable because of changes made to theuser's weight management or fitness program.

The above algorithms may utilize baselines, such as those presentedelsewhere in this disclosure (e.g., such as like the algorithmsdescribed with respect to FIGS. 18A-22). Instead of M4>M3, for example,M4>Baseline may be used. Additionally, as was described earlier, insteadof subtraction, a ratio may be used. In the sensor literature, a signalto noise ratio (SNR) of 3 or more is desirable. In many clinical (orphysiological) situations, however, an SNR of 2 is considered desirable,and sometimes even less.

Example 3

Comparing trigger points to ketone measurements may require use ofcompensation factors, such as those described in U.S. patent applicationSer. No. 14/690,756 commonly owned by the applicant and which isincorporated by reference herein. For example, it is generally knownthat a woman's body temperature is slightly higher during ovulation. Insome instances, because of the increased temperature, there is increasedkinetic/metabolic activity, which can cause an increase in ketonelevels. If comparing two measurements (one the day before and one theday after ovulation begins), it may be necessary to adjust thepost-ovulation measurement to account for the increase. In so doing, thedata is normalized so that trigger points can be more readilyrecognized. Algorithms described in U.S. patent application Ser. No.14/690,756 may be used for this purpose.

Example 4

The trigger point state aids in determining the likelihood that atrigger point occurred. There are instances in which overwhelmingevidence that a trigger point state has been achieved overrides theotherwise driving algorithms. For example, if M5>M4 (which ordinarilydoes not trigger a prompt to a user requesting confirmation of a triggerpoint), if M5 occurred on a day when the user was at a salt water taffyshop and this user has a known problem with salt water taffy, the usermay be prompted to confirm the trigger point. This type of approach isparticularly useful in the following situations: (1) the user istraveling and forgot his or her ketone measurement device at home, butinterpretation of the measurements when the user returns home isdependent on knowing if the user deviated during the trip; (2) there isa delayed decrease in the ketone measurement as a result of the triggerpoint. For example, if the user's heart rate is elevated 3-4 timesduring a 24-hour period, this might indicate high levels of stress dueto a social or family situation, but the stress-impact to ketonemeasurements may not be noticeable until cortisol levels have changedsome 48-72 hours later; and/or (3) the user has already experienced asignificant trigger point and ketone measurements are low in general. Inthis type of situation, to aid in predicting the time period torecovery, counting the number of trigger points is useful. To exemplifythis, a user's consumption of a high carbohydrate food (e.g., 1 bag ofpotato chips) may be enough to drop ketone levels to zero but recoverymay occur within 2 days. However, if the user then consumes five timesthe volume (e.g., 5 other bags of potato chips), sufficient glycogenand/or glucose may be stored (from the multiple bags of chips) and theuser will not return to a state of ketosis for a longer than normalperiod of time. Knowing the quantity of potato chips consumed is usefulpredictively for the ketone measurement system.

The algorithms for detecting the correlations between breath acetonelevels and the occurrence of trigger points may be embodied in themobile application run on the electronic device, in software executed bythe remote system, and/or in software executed by the measurementdevice.

Once a pattern is determined, future breath acetone measurements can beanalyzed (e.g., by the measurement device, by the mobile applicationrunning on the electronic device, etc.) to identify whether the patternor a close match of the pattern (e.g., within a threshold value orpercentage) has resulted as described in the examples above. Thus, theoccurrence of a user-specific trigger point may be identified evenwithout any explicit indication from the user or a third party that sucha trigger point has occurred.

Preferably, the method comprises sending a message or alert to acaregiver or another third party via a network if a pre-specified alarmnumber of predictions occur. For example, the App may generate anotification to be transmitted to a caregiver or another third party ifa certain number of trigger points are determined to have occurredwithin a set period of time.

A pre-specified alarm number may be set to mark the tolerance foruser-specific trigger points to occur and cause unacceptable behavior.As an example, if a user has a challenge overeating popcorn when goingto a movie theater, each time the user is at a movie theater and his orher breath acetone levels drop, a prediction may be generated suggestingthat the user consumed popcorn. If the pre-specified alarm number is twotimes a month, two popcorn violations may cause the physician to benotified.

Alternatively, the method may comprise changing the treatment program ifa pre-specified alarm number of predictions occur. This is importantbecause if behavioral modification alone is not sufficient to controlthe user's actions, the treating physician may need to administer drugs,therapy, consider a different diet that is better suited for the user'sphysiology, or other intervention.

Using Non-Ketone Biometric Data with Rule Compliance Detection, AcetoneTags, and Trigger Points

In some embodiments, the system (e.g., the measurement device, theelectronic device, the remote system, or another diagnostic ormeasurement device not shown in FIGS. 1-3) may be capable of monitoringthe heart rate and/or other non-ketone biometric parameters of the user.For example, in embodiments that use a mobile application running on asmartphone, the smartphone may communicate by Bluetooth, Wi-Fi, oranother wireless standard with a user-worn heart rate monitoring devicesuch as a wristband or watch. Through this wireless connection, themobile application may monitor the user's heart rate in real time, ormay retrieve heart rate statistics or history data. As another example,the mobile application may run on a smart watch or other user-worndevice that is capable of directly monitoring the user's heart rate, andthe smartphone may be omitted.

In these embodiments, some or all of the above-described featuresinvolving rules, acetone tags, and trigger points may be varied to makeuse of the additional biometric data. As one example, a rule may requirethat a user engage in a certain level of aerobic exercise within aprescribed time frame before taking an acetone measurement. To evaluatecompliance with this rule, the mobile application (or another systemcomponent) may determine whether the user's heart rate reached a certainthreshold level during the relevant time frame preceding the acetonemeasurement. As another example, the system may use the monitored heartrate to generate rule-based reminders; for instance, where a rulerequires aerobic activity within a prescribed time period before anacetone measurement, the mobile application may generate a measurementreminder upon detecting that the user's heart rate has reached a targetexercise level and has then dropped to a non-exercise level. As yetanother example, the system may automatically record, in associationwith a specific acetone level measurement, one or more heart ratestatistics, such as the user's average and peak heart rates over aparticular time period, such as the last 2 hours. As yet anotherexample, a rule may require that the user take the ketone measurement assoon as the user wakes up. The mobile application may receiveinformation from a pedometer (or an application running on theelectronic device that functions as a pedometer using sensors present inthe electronic device) that indicates a number of steps that the userhas walked in the current day. If the number of steps is low (e.g., lessthan 10 steps), then the mobile application may automatically determinethat the user has complied with the rule.

As another example, an acetone tag applicable to a user may be whetherthe user has engaged in exercise during a time immediately preceding theketone measurement (e.g., button 510 in FIG. 13 discussed above). Themobile application may automatically select the acetone tag for the nextketone measurement if the user's heart rate exceeded a minimum level inthe time immediately preceding the ketone measurement (e.g., the minimumlevel was exceeded within a 2 hour period before the ketonemeasurement), which may indicate that the user was exercising. Likewise,another acetone tag applicable to a user may be whether or not the userhas been in a high stress condition prior to the ketone test (e.g.,button 512 in FIG. 13 discussed above). The mobile application mayautomatically select the acetone tag for the next ketone measurement ifthe user's heart rate exceeded a minimum level in the time immediatelypreceding the ketone measurement (e.g., the minimum level was exceededwithin a 2 hour period before the ketone measurement), which mayindicate that the user was experiencing a high level of stress.

As another example, a trigger point applicable to a user may be“drinking coffee.” In some cases, the user's heart rate may rise to atarget heart rate level when the user drinks coffee. Thus, the mobileapplication may automatically generate a prompt to ask the user whetherthe user drank coffee prior to the ketone measurement test if the heartrate meets or exceeds the target heart rate level.

Communications

Before any communications described herein are sent between the breathanalysis device and the electronic or user device (e.g., smartphone),the electronic device may pair with the breath analysis device usingtechniques described in U.S. Provisional Patent Application No.62/161,872, titled “USER AND BREATH ANALYSIS DEVICE PAIRING ANDCOMMUNICATION” and filed on May 14, 2015, which is hereby incorporatedherein by reference in its entirety.

Either the communication device (e.g., electronic device) or the breathanalysis device may retain a local copy of the transmitted data suchthat the data resides both on the remote system as well as thecommunication device and/or the breath analysis device.

The system between the breath analysis device and the remote system mayalso facilitate a “mandatory uplink” feature. The mandatory uplinkfeature may cause the breath analysis device to transmit data (e.g.,readings) to the electronic device, and the electronic device mayautomatically transmit the data to the remote system, without any userinteraction upon receipt of the data from the breath analysis device.This feature may also involve withholding output or display of themeasurement signal (e.g., the readings) until the signal has beentransmitted to and/or received by the remote system. The mandatoryuplink feature desirably prevents users from selectively reporting orwithholding certain test results. For example, in the context of aweight loss program in which reported results are reviewed by a coach,some users may wish to report only the “good” test results.

In some embodiments, the mandatory uplink feature includes a selectivetransmission of data based on one or more factors. For example,conditions may be placed on a location in which the user is allowed totake readings. Thus, the electronic device may use a GPS location todetermine whether to forward received data to the remote system. Asanother example, conditions may be placed on a time in which the user isallowed to take readings. Thus, the electronic device may use a time(e.g., wall clock time) to determine whether to forward received data tothe remote system. As another example, conditions may be placed on thetype of physiological state the user must be in when the readings aretaken. Thus, the electronic device may use the physiological state ofthe user (e.g., heart rate, whether the user is asleep or awake, etc.)to determine whether to forward the received data to the remote system.Alternatively, the electronic device may forward the data regardless oflocation, time, and/or physiological state of the user, but may use theGPS location, time, and/or physiological state of the user to determinewhether to mark the data as normal or aberrant.

The breath analysis device and/or the electronic device may furthercache data before the data is forwarded to the remote system. Forexample, the breath analysis device and the electronic device may lose aconnection with each other (e.g., because the electronic device is movedout of transmission range of the breath analysis device). The breathanalysis device may then cache the data at least until connectivity withthe electronic device is restored and the cached test resultstransferred. As another example, the electronic device may receive datafrom the breath analysis device, but may not be in networkedcommunication with the remote system. Thus, the electronic device maycache the received data at least until the electronic device cancommunicate with and transmit the data to the remote system.

This mandatory uplink feature can be advantageous for several reasons.As an example, the remote system may be consulted to improve themeasurement process (e.g., use different parameters given a user'sresponse). In such a case, the remote system processes the measurementsignal, generates a response signal in response to the measurementsignal, and transmits the response signal to the breath analysis device.This response signal can be useful to control or modify an operatingparameter for instrumentation within the breath analysis device, such aspumps, linear actuators, or sensors. As a further example, mandatoryuplink can be useful to ensure that parameter data is stored and madeavailable to facilitate broader population data. “Population Data” isused broadly to mean qualitative or quantitative information pertainingto the aspect of the relevant analyte or measurement that is beingreported, where the information has been collected from a plurality ofindividuals. Such information is often useful to establish populationreference ranges, trends or changes, or predictions. It may be desirablefor the plurality of individuals to represent a statisticallysignificant sample, for example of a particular ethnicity, sex, healthstatus, or other stratifying characteristic.

Population data aids in establishing parameters such as normalvariability, ranges of acceptable and pathological values, and trends,and the like. As yet another example, mandatory uplink can be useful tocheck the user's measurement signal against historical signals to assesswhether the user's measurement signal appears accurate or whether ameasurement may have occurred. This would result in a user prompt foradditional information regarding, for instance, the user's state.

In view of the foregoing, there are various new methods for displayingketone measurement data that facilitate the principles and innovationsset forth herein. For example, the ketone measurement data may bemanipulated to form statistical results and the statistical results maybe displayed. Examples of such statistical results are provided in FIGS.18A-22. The manipulated ketone measurement data illustrated in FIGS.18A-22 may be generated by the electronic device 130 and displayedwithin a user interface. The manipulated ketone measurement dataillustrated in FIGS. 18A-22 may alternatively or in addition bedisplayed in a content page generated by the remote system 140. A usermay access the data by visiting the content page using, for example, abrowser running on the electronic device 130.

FIGS. 18A-E present breath acetone measurement results data for fiveestablished dieters over a 50-day weight loss program. At the bottom ofthe charts illustrated in FIGS. 18A-E, various formulas are shown thatcan be used to display the data in different formats.

For example, as illustrated in FIG. 18A, Algorithm 1 may include abreath acetone level measured on a first day (e.g., a day before thecurrent day) subtracted from the breath acetone level measured on asecond day (e.g., the current day). In Algorithm 1, R2 may represent thebreath acetone level measured on a current day and R1 may represent abreath acetone level measured on the day before the current day.

As another example, as illustrated in FIG. 18B, Algorithm 2 may includea multi-day running average (e.g., a 3-day running average) of breathacetone levels subtracted from another multi-day running average (e.g.,another 3-day running average) of breath acetone levels. In Algorithm 2,Avg(R1, R2, R3) may refer to a 3-day running average of breath acetonelevels for a day after the current day (e.g., R1), two days after thecurrent day (e.g., R2), and three days after the current day (e.g., R3).Avg(R0, R-1, R-2) may refer to a 3-day running average of breath acetonelevels for a day after a current day (e.g., R0), a day before thecurrent day (e.g., R-1), and two days before the current day (e.g.,R-2). A multi-day running average may be useful in smoothing varyingbreath acetone measurements to provide the user with more meaningfulresults. For example, a user may exercise every other day, therebyresulting in large variations in the breath acetone levels day by day.Because of the large variations, displaying the raw breath acetonemeasurements may not provide a clear indication of whether the user ispositively progressing to a desired goal. A running average, on theother hand, may provide a clearer indication of whether the user ispositively progressing to the desired goal.

As another example, as illustrated in FIG. 18C, Algorithm 3 may includea baseline breath acetone level subtracted from a multi-day runningaverage (e.g., a 3-day running average) of breath acetone levels. InAlgorithm 3, Avg(R-1, R0, R1) may refer to a 3-day running average ofbreath acetone levels for a day before the current day (e.g., R-1), acurrent day (e.g., R0), and a day after the current day (e.g., R1).

As another example, as illustrated in FIG. 18D, Algorithm 4 may includea breath acetone level measured on a first day (e.g., a day before thecurrent day) subtracted from the breath acetone level measured on asecond day (e.g., the current day), with that difference divided by thebaseline breath acetone level. In Algorithm 4, R2 may represent thebreath acetone level measured on a current day and R1 may represent abreath acetone level measured on the day before the current day.

As another example, as illustrated in FIG. 18E, Algorithm 5 may includea breath acetone level measured on a first day (e.g., a day before thecurrent day) subtracted from the breath acetone level measured on asecond day (e.g., the current day), with that difference divided by thebreath acetone level measured on the first day. In Algorithm 5, R2 mayrepresent the breath acetone level measured on a current day and R1 mayrepresent a breath acetone level measured on the day before the currentday.

As another example, another algorithm illustrated in FIG. 19 (e.g.,referred to herein as Algorithm 6) may include a baseline breath acetonelevel subtracted from a multi-day running average (e.g., a 3-day runningaverage) of breath acetone levels, with that difference divided by thebaseline breath acetone level. In the algorithm, Avg(R-1, R0, R1) mayrefer to a 3-day running average of breath acetone levels for a daybefore the current day (e.g., R-1), a current day (e.g., R0), and a dayafter the current day (e.g., R1).

Optionally, the raw baseline may be displayed along with plotted acetonelevels. The raw baseline and/or the plotted acetone levels (e.g., theraw acetone measurements and/or the data resulting from implementationof one or more of the above described algorithms) may be transmitted toan external device, such as a device operated by a physician or similarthird party support person, via the App.

In some embodiments, the measurement device 112 and/or the electronicdevice 130 (e.g., via the App) selects one or more of the algorithms anddisplays data based on the selected algorithm(s). For example, each ofthe above-described algorithms, when plotted, may provide usefulinformation for only a subset of users. Data plotted based on Algorithm1 for a first user may provide useful information to the first userand/or a third party, but data plotted based on Algorithm 2 for thefirst user may not provide useful information to the first user and/orthe third party. Thus, the measurement device 112 or the electronicdevice 130 (e.g., via the App) may select and plot data corresponding toalgorithms that may provide useful information to the respective user.

The App may select an algorithm based on the occurrence of user-reportedevents and/or based on characteristics of the user. For example, the Appmay generate a different graph based on one or more of the algorithmsand the raw breath acetone measurements. If the user indicates that anevent occurred, the App may analyze the values in each graph around atime that the event occurred. The App may select the algorithms forwhich there is a change in values before and after the time that theevent occurred that is greater than a threshold value. As anotherexample, the App may receive and store characteristics of the user. Suchcharacteristics may include biographical information, how often the userexercises, the intensity of such exercise, and/or the like. Combinationsof these characteristics may be correlated with a specific algorithm orspecific algorithms. Based on the combination of characteristics storedfor a particular user, the App may select one or more algorithms andgenerate a different graph based on the selected algorithms for displayin the App.

FIG. 19 shows a response of five established dieters to a temporarychange in the diet program that is known to cause increased ketonelevels. For example, the baseline breath ketone level for each of thefive users may be 12.8 ppm, 26.4 ppm, 18.1 ppm, 12.7 ppm, and 11.7 ppm,respectively. Because each user has different baseline breath ketonelevels, the App may normalize the ketone levels such that differencesbetween measured ketone levels and the baseline can be visualized. Forexample, the App may use Algorithm 6 to normalize the ketone levels. Asillustrated in FIG. 19, users 1, 2, and 5 showed clinically positiveresponses to the temporary diet change, which is reflected in normalizedketone levels that are above 0. Users 3 and 4 did not report successwith the change in the diet program, which is reflected in their lownormalized levels of ketones (e.g., normalized ketone levels around 0).

FIG. 20 shows the same data as is presented in FIG. 19, but highlightssignificant variations in the data. For example, the App may highlightnormalized ketone levels that are a threshold value (e.g., a specificvalue, a standard deviation, two standard deviations, etc.) greater thana set value (e.g., a median or mean normalized ketone level, 0, etc.).The normalized ketone levels may be highlighted by being displayed in acolor different from the color of other normalized ketone levels thatare closer to the set value, as a symbol different from the symbol usedto depict other normalized ketone levels that are closer to the setvalue, as being enclosed within a shape (e.g., an oval, a rectangle,etc.), and/or the like. As illustrated in FIG. 20, the normalized ketonelevels that are the threshold value greater than the set value areenclosed in ovals A, B, C, D, and E.

FIG. 21 shows the response of five individuals who were starting off ona diet program. For example, the baseline breath ketone level for eachof the five users may be 1.72 ppm, 1.52 ppm, 0.80 ppm, 1.96 ppm, and0.44 ppm, respectively. Because each user has different baseline breathketone levels, the App may normalize the ketone levels such thatdifferences between measured ketone levels and the baseline can bevisualized. For example, the App may use Algorithm 6 to normalize theketone levels, as described above with respect to FIG. 19.

FIG. 22 shows the same data as is presented in FIG. 21, but highlightssignificant variations in the data. For user 7, events A, B, and Dreflect non-compliance. Event E shows the effect of exercise. Event Cshows the user's processed ketone levels in response to compliance withthe diet program. For user 8, event F reflects the addition of exerciseand event G reflects non-compliance. For user 9, event H reflects theuser's difficulty controlling the user's diet for several days after abirthday party. Event I reflects the user's improved ketone levels inresponse to a change in the diet program. Event J reflects the user'snon-compliance. For user 10, although the data seems to suggest that theuser outperformed the user's baseline, it is important to note that theuser's baseline was very low (within the normal range) and that all rawlevels were within the normal range. This is an instance in which boththe baseline and the processed data should be consulted in tandem. In anembodiment, the App may generate the graphs illustrated in FIG. 22 withannotations identifying and/or describing the various events A-Kdescribed above.

Although these processes are useful, this may be too much informationfor a given user. The App may provide a healthcare provider may have theoption to see more data or to limit the presentation formats of data tothe user.

In some embodiments, the measurement device 112 and/or the electronicdevice 130 (e.g., via the App) update the values of previous breathacetone measurements based on the values of current breath acetonemeasurements. For example, the App may include a graph that plots breathacetone measurements (or some manipulation of the measurements based onone of the above-described algorithms). As new breath acetone levels aremeasured, the App may update the graph by revising the values of thepreviously plotted breath acetone measurements. The values may berevised, for example, such that the graph is more easily understood bythe user. In some cases, breath acetone levels may drop due to a triggerpoint or another event. The drop in breath acetone levels may be gradualand occur over several days because some acetones may take several daysto be flushed from the user's body. The user, however, may expect thedrop to be sudden and may not understand why the plotted graph shows agradual drop in breath acetone levels. Thus, if the measured breathacetone levels show a downward trend, with the currently measured breathacetone level at or near an expected trough, the App may revise thegraph such that the breath acetone levels measured in one or more daysbefore the current day are depicted as having values that match ornearly match the measured breath acetone level of the current day.

Terminology

All of the actions described herein as being performed by an “electronicdevice” may be performed under the control of a mobile application, suchas the mobile application 2715. All of the methods and tasks describedherein may be performed and fully automated by a computer system. Thecomputer system may, in some cases, include multiple distinct computersor computing devices (e.g., physical servers, workstations, storagearrays, cloud computing resources, etc.) that communicate andinteroperate over a network to perform the described functions. Eachsuch computing device typically includes a processor (or multipleprocessors) that executes program instructions or modules stored in amemory or other non-transitory computer-readable storage medium ordevice (e.g., solid state storage devices, disk drives, etc.). Thevarious functions disclosed herein may be embodied in such programinstructions, and/or may be implemented in application-specificcircuitry (e.g., ASICs or FPGAs) of the computer system. Where thecomputer system includes multiple computing devices, these devices may,but need not, be co-located. The results of the disclosed methods andtasks may be persistently stored by transforming physical storagedevices, such as solid state memory chips and/or magnetic disks, into adifferent state. In some embodiments, the computer system may be acloud-based computing system whose processing resources are shared bymultiple distinct business entities or other users.

Depending on the embodiment, certain acts, events, or functions of anyof the processes or algorithms described herein can be performed in adifferent sequence, can be added, merged, or left out altogether (e.g.,not all described operations or events are necessary for the practice ofthe algorithm). Moreover, in certain embodiments, operations or eventscan be performed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors or processor cores or onother parallel architectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, andalgorithm steps described in connection with the embodiments disclosedherein can be implemented as electronic hardware (e.g., ASICs or FPGAdevices), computer software that runs on general purpose computerhardware, or combinations of both. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, and steps have been described abovegenerally in terms of their functionality. Whether such functionality isimplemented as specialized hardware versus software running ongeneral-purpose hardware depends upon the particular application anddesign constraints imposed on the overall system. The describedfunctionality can be implemented in varying ways for each particularapplication, but such implementation decisions should not be interpretedas causing a departure from the scope of the disclosure.

Moreover, the various illustrative logical blocks and modules describedin connection with the embodiments disclosed herein can be implementedor performed by a machine, such as a general purpose processor device, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or otherprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof designed to perform thefunctions described herein. A general purpose processor device can be amicroprocessor, but in the alternative, the processor device can be acontroller, microcontroller, or state machine, combinations of the same,or the like. A processor device can include electrical circuitryconfigured to process computer-executable instructions. In anotherembodiment, a processor device includes an FPGA or other programmabledevice that performs logic operations without processingcomputer-executable instructions. A processor device can also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Although described herein primarily with respect todigital technology, a processor device may also include primarily analogcomponents. For example, some or all of the rendering techniquesdescribed herein may be implemented in analog circuitry or mixed analogand digital circuitry. A computing environment can include any type ofcomputer system, including, but not limited to, a computer system basedon a microprocessor, a mainframe computer, a digital signal processor, aportable computing device, a device controller, or a computationalengine within an appliance, to name a few.

The elements of a method, process, routine, or algorithm described inconnection with the embodiments disclosed herein can be embodieddirectly in hardware, in a software module executed by a processordevice, or in a combination of the two. A software module can reside inRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory,registers, hard disk, a removable disk, a CD-ROM, or any other form of anon-transitory computer-readable storage medium. An exemplary storagemedium can be coupled to the processor device such that the processordevice can read information from, and write information to, the storagemedium. In the alternative, the storage medium can be integral to theprocessor device. The processor device and the storage medium can residein an ASIC. The ASIC can reside in a user terminal. In the alternative,the processor device and the storage medium can reside as discretecomponents in a user terminal.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without other input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z).Thus, such disjunctive language is not generally intended to, and shouldnot, imply that certain embodiments require at least one of X, at leastone of Y, and at least one of Z to each be present.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it can beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As can berecognized, certain embodiments described herein can be embodied withina form that does not provide all of the features and benefits set forthherein, as some features can be used or practiced separately fromothers. The scope of certain embodiments disclosed herein is indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A breath analysis system comprising: a breathanalysis device comprising a breath input port, a ketone sensor, and awireless transceiver, the breath analysis device configured to generatebreath ketone measurements representing breath ketone levels in breathsamples of a user, the breath analysis device further configured totransmit the breath ketone measurements to a mobile device of the user;and a data processing system comprising a mobile application that runson the mobile device, the data processing system configured to generatea baseline ketone level based on a plurality of breath ketonemeasurements generated by the breath analysis device during a first timeperiod that precedes the user starting a health program, the dataprocessing system further configured to generate, and display on themobile device, a user interface that displays an indication of howbreath ketone measurements taken while the user is on the health programcompare to the baseline ketone level.
 2. The breath analysis system ofclaim 1, wherein the user interface displays a graph of a plurality ofbreath ketone measurements taken while the user is on the healthprogram, the graph including a representation of the baseline ketonelevel.
 3. The breath analysis system of claim 1, wherein the dataprocessing system is configured to subtract the baseline ketone levelfrom breath ketone measurements taken while the user is on the healthprogram.
 4. The breath analysis system of claim 1, wherein the dataprocessing system comprises a server that communicates with the mobileapplication.
 5. The breath analysis system of claim 1, wherein theplurality of breath ketone measurements are taken over a correspondingplurality of days.
 6. The breath analysis system of claim 5, wherein thedata processing system is configured to generate the baseline ketonelevel by taking an average of the plurality of breath ketonemeasurements.
 7. The breath analysis system of claim 1, wherein themobile application comprises a measurement tagging user interface thatenables the user to tag an individual ketone measurement with apre-specified indication of a condition that existed at a time of theketone measurement.
 8. The breath analysis system of claim 1, whereinthe ketone sensor is a nanoparticle-based sensor.
 9. A method ofgenerating and analyzing ketone measurements of a user, the methodcomprising: during a first period of time that precedes the userstarting a health program, generating a plurality of ketone measurementswith a bodily fluid analysis device that comprises a ketone sensor, theketone measurements representing ketone levels in bodily fluid samplesof the user; by execution of program code by a data processing system,generating a baseline ketone level for the user based on the pluralityof ketone measurements; during a second time period during which theuser is on the health program, generating additional ketone measurementswith the bodily fluid analysis device based on additional bodily fluidsamples of the user; and generating a user interface that displays theadditional ketone measurements relative to the baseline ketone level.10. The method of claim 9, wherein the user interface displays a graphof a plurality of ketone measurements taken while the user is on thehealth program, the graph including a representation of the baselineketone level.
 11. The method of claim 9, further comprising, by the dataprocessing system, subtracting the baseline ketone level from ketonemeasurements taken while the user is on the health program.
 12. Themethod of claim 9, wherein the plurality of ketone measurements aretaken over a corresponding plurality of days.
 13. The method of claim12, wherein generating the baseline ketone level comprises taking anaverage of the plurality of ketone measurements.
 14. The method of claim9, wherein the user interface comprises a measurement tagging userinterface that enables the user to tag an individual ketone measurementwith a pre-specified indication of a condition that existed at a time ofthe ketone measurement.
 15. The method of claim 9, wherein ketonemeasurements are generated by analyzing respective breath samples of theuser.